The Patient Matters in the End(point)

Area of Science:

• Digital health
• Clinical trial methodology
• Biomarker development

Background:

• Wearable sensors and digital health technologies are prevalent in clinical trials.
• Current endpoints derived from these technologies are often raw metrics or opaque AI-driven biomarkers.
• This limits meaningful interpretation of patient health improvements.

Purpose of the Study:

• To advocate for a patient-centered approach in designing digital health endpoints for clinical trials.
• To enhance the interpretability and patient relevance of data collected via digital health technologies.

Main Methods:

• Review of current practices in digital health endpoint generation.
• Conceptual framework for patient-centered endpoint design.
• Emphasis on patient-centricity from the initial stages of trial design.

Main Results:

• Current methods often produce endpoints that are difficult to link directly to patient health outcomes.
• AI-driven biomarkers, while predictive, lack transparency for patients.
• A patient-centered design can yield more interpretable and meaningful endpoints.

Conclusions:

• Designing clinical trial endpoints with digital health technologies requires a patient-first perspective.
• This approach ensures that data collected directly benefits patient understanding of treatment implications.
• Prioritizing patient-centricity enhances the value and interpretability of digital health data in trials.