Science Blog

Sleep-related outcome measures obtained with wearable sensors provide valuable data in clinical trials. In addition to reducing the tremendous burden and cost associated with the traditional methods of assessing sleep, they offer an opportunity to take a more in-depth, day-to-day look into how a patient feels, functions, and survives.

We really like validation: in fact, you might say we take it personally. It’s the foundation of everything we do. We are excited to report that we’re nearing the completion of a study that we believe will greatly expand the possibilities of digital outcome measures in clinical trials.

The research includes steps 1 and 2 of the V3 framework, verification and analytical validation. The outcome will be validated, machine learning algorithms to derive novel, real-world measures of physical function and mobility from wearable kinematic sensors (e.g., accelerometers, gyroscopes). Here’s an overview of our approach to the study and development efforts.

COAs, Biomarkers, and Endpoints, Oh My!

Establishing a common vocabulary accelerates progress by enabling effective communication and collaboration among stakeholders and facilitating cross-study comparisons and evidence harmonization. However, when we work with clinical researchers, they often use the words biomarkers, clinical outcome assessments (COA’s), and Endpoints interchangeably, but technically they mean different things.

I’m confident that my amazing team members often think I’m crazy when I deliberate or nitpick over the use of a single word. The point is words matter, especially in science and definitely in a new field like digital medicine. As this field evolves, definitions and key terms must be used consistently to reduce the hindrance of evaluating and interpreting scientific evidence.

Wearable technologies and their associated informatics platforms gather, store, and process vast amounts of health-related, real-world data. These datasets can be some of the most complex used in health research. If the end-goal is to provide evidence in regulatory decision making, implementing well-defined practices to demonstrate sufficient data quality and fidelity is a must.

When it comes to the development and discovery of novel digital biomarkers in clinical trials, one size does not fit all. Patrick Hankey, our head of strategic business development, sums up the problem very well when he explains, “If you have seen one clinical trial, you have seen one clinical trial.” In this article, we describe what we mean by “one size does not fit all” within the context of digital biomarker discovery and provide examples of how this principle is particularly relevant to the validation of wearable sensor solutions.

The first guiding principle to the development and discovery of novel digital biomarkers is hypothesis-driven research. In this article, we will describe our approach and provide examples of how we use this principle to ensure robust digital biomarker development.