From surveys to sensors, we develop digital measures that meet scientific and clinical standards.
Digital Clinical Measures are quantifiable indicators of health collected via either:
- Digital Survey Processes – structured, repeated self-reports using validated instruments
- Connected Sensor Processes – physiological or behavioral signals captured via devices
While many focus primarily on either sensor-derived data or survey-based inputs, we specialize in combining both—applying state-of-the-art data fusion to develop robust digital endpoints.
From Health to Endpoint: Our 3-Step Process
We support clients in linking a meaningful health aspect to a digital measure through a structured process:
- Determine the meaningful aspect of health (MAH)
- Identify the concept of interest (COI)
- Define the digital measure (e.g., as an outcome or endpoint)
This process is applicable across survey-based, sensor-based, or hybrid data sources.
What We Support
We work closely with clients and partners to validate digital measures across both domains:
Digital Survey Process
- Criterion validation – performed collaboratively with clients
- Construct validation – developed jointly
- Content validation – co-designed with clients
Connected Sensor Process
- Verification – not in current scope
- Usability validation – with clients or technology partners
- Analytical validation – executed in-house
- Clinical validation – in collaboration with clients or clinical partners
Whether your measure originates from surveys, sensors, or both, we support its development toward regulatory and scientific acceptance.
Example Use Case: Mood Transitions in Depression
From brain anatomy (left) to complex network representations (center) and simplified dynamic models (right), our approach connects biological insight with computational clarity.
Early warning signals of depression onset or recovery—such as increased autocorrelation, variance, and emotional cross-correlations—can be detected through smartphone-based mood self-tracking.
These dynamic markers, rooted in the theory of critical slowing down, illustrate how high-frequency digital self-report can serve as the basis for predictive digital biomarkers—without relying on passive sensor data.
This is just one way we help clients turn survey-based processes into validated, actionable clinical measures.
Let’s explore how your digital measure could move forward.
Contact us to start a conversation.
