The Patient Matters in the End(point)

Abstract

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. The paper represents a call to arms to really place the patient at the heart of the endpoint. 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.

Keywords: Clinical outcomes assessments; Clinical trial endpoints; Digital health technologies; Patient centricity; Wearable sensors.

Fig. 1

A proposed pathway to defining a digital COA endpoint. This pathway is iterative in the sense that information gained in later stages may need to be explored again in the earlier stages of the work. For example, if as a result of the patient interviews, a device measuring walking bouts is chosen, it may be necessary to interview patients again to see their understanding of how walking bouts may relate to their life. Alternatively, if a device is selected, but perhaps has poor analytic validation, the research team could decide to re-review potential available devices rather than develop their own algorithm