Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption

Abstract

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as “endpoints.” A “digital endpoint” is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient’s free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone’s microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer’s disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug’s effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient’s experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

The next generation of evidence-based medicine

“…In addition to biomarkers, every field needs to define areas of top priority for research and identify the most relevant endpoints to answer priority research questions. Endpoints are measures of health and/or disease and serve different purposes depending on the phase of the trial 28,29 . Beyond clinical and regulatory endpoints, patient-reported outcomes and digital endpoints are also rapidly emerging.…”

“…Digital endpoints. Digital endpoints are sensor-generated data collected outside the clinical environment in the context of patients' routine living-such as using smartphone microphones to monitor cognitive decline in people with Alzheimer's disease or smartwatch monitors to evaluate drug effect in people with sickle-cell anemia 29 . This is an area of considerable excitement in medicine as it could permit more realistic real-world tracking of the patient experience.…”

Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies

“…It is essential to consider the multiple stakeholders (as in Tables 2, 3), study scope and objectives, and the intended application of the outcomes [23]. Digital Endpoints Endpoints need to be reliable and accurate, measure the treatment effects, validated against an appropriate reference standard, and assess the population under consideration [37][38][39][40]. A fundamental challenge of BYOD models is ensuring the equivalence of digital endpoints captured or derived from different technologies.…”

“…Endpoints need to be reliable and accurate, measure the treatment effects, validated against an appropriate reference standard, and assess the population under consideration [ 37 , 38 , 39 , 40 ]. A fundamental challenge of BYOD models is ensuring the equivalence of digital endpoints captured or derived from different technologies.…”

Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball

“…However, the pharmaceutical R&D community is cautious of adopting digital endpoints until they are fully proven [ 8 ]. Our project aims to develop a business tool to help close this gap, using PD as the exemplar disease with high unmet clinical need and significant technology potential.…”