Journey to the Right Digital Endpoint: A Framework to Derive Reliable Clinical Outcome Scores from Continuous DHT Data

Overview

While there are many advantages to using digital health technologies (DHTs) to collect data for clinical trial endpoints, there are also challenges that arise from the complex nature of this data. The large volume of data can be overwhelming, and it can be challenging to make informed decisions about which measures to use as endpoints. 

Here, we describe the unique considerations that come with data collected from sensor-based DHTs. Then, we outline the key information needed to evaluate different digital measures and share a framework to optimize the measurement properties for the use as clinical endpoints.  

We use digital measures of physical functioning for diabetes research as an example to illustrate how this framework could facilitate the use of patient-centric digital measures in clinical trials.

Related Resources:

• Digital Endpoints Series: Obesity and Related Metabolic Disorders
• Accelerating DHT Research and Drug Development with Open-source Big Data from Population Studies

What You'll Learn

• Differences in data complexity between traditional assessments and DHTs
• What you need to know when choosing a DHT-based endpoint
• A digital clinical endpoint evaluation framework
• A case study detailing how this evaluation framework was applied to determine which physical activity digital measure is most robust and appropriate to differentiate between groups of individuals with diabetes