The digital health sector is full of real-time data, from heart rate and sleep patterns to stress levels and glucose trends. However, not every number collected by a wearable or app is a digital biomarker. To become a trusted clinical indicator, these digital measures need to pass strict scientific and regulatory checks. This process, called clinical validation, turns ordinary data into tools that can be used in trials, diagnostics, and patient monitoring.
In recent years, digital biomarkers have become key to innovation in clinical trials and real-world evidence. They allow for continuous, remote, and objective tracking of physiological processes, which traditional medicine could only measure during occasional tests. Still, for product teams working on new digital health solutions, showing that these biomarkers are clinically valid is one of the toughest challenges.
Previously, we talked about the most important biomarkers. Today, in this article, we will look at what makes a digital biomarker clinically sound, explain the clinical validation process, outline the testing phases, and show how companies like Thryve help create strong and compliant digital biomarkers.
Digital biomarkers are measurable, objective data points about physiology and behavior collected through connected devices, such as wearables, smartphones, implantables, and medical sensors. Unlike traditional biomarkers that depend on lab samples or invasive procedures, digital biomarkers gather health data continuously in real-world settings. This helps researchers, clinicians, and developers better understand how the body works outside the clinic.
A digital biomarker is valuable when it links a digital signal, such as heart rate variability, activity levels, or sleep duration, to a proven physiological outcome like stress resilience, cardiovascular fitness, or metabolic risk. In clinical trials, these digital measurements are changing how researchers track results, find participants, and monitor adherence. For example, tracking movement over time can reveal early signs of neurological disorders before symptoms show, and changes in HRV can point to higher stress or cardiovascular risk.
Beyond research, digital biomarkers are transforming chronic disease management, preventive care, and digital therapeutics. They provide a feedback loop that connects lifestyle behaviors to biological responses. It’s a foundation for personalized, data-driven healthcare.
However, for these metrics to carry clinical weight, they must undergo rigorous validation to ensure accuracy, reliability, and real-world relevance. That’s where the process of clinical validation comes in, turning raw data into trusted medical evidence.
To make a digital biomarker clinically valid, it must prove not just that it works, but that it works reliably, meaningfully, and safely. In other words, the signal measured by a wearable or app must consistently represent a true physiological process and link to real clinical outcomes. This proof is built through a structured process called clinical validation, which rests on four core pillars of evidence.
Before a biomarker can be trusted in a clinical setting, the data itself must be accurate and reproducible. Analytical validation asks: Does the device measure what it claims to measure, and can it do so consistently?
This stage evaluates factors such as signal quality, sensor precision, repeatability across environments, and robustness against user variability. For instance, heart rate data from a wearable should match medical-grade ECG readings within a defined margin of error.
Once data reliability is proven, clinical validation tests whether the digital biomarker correlates with real-world medical outcomes. For example, does reduced step count correlate with heart failure progression? Does HRV reflect stress load or autonomic balance? Clinical validation typically involves comparative studies against established gold-standard measures and often spans multiple patient populations to ensure generalizability.
Digital biomarkers that inform diagnosis or treatment must align with medical regulatory frameworks such as the EU MDR, FDA Digital Health Framework, or ISO 13485 standards. Regulatory validation ensures that data management, consent, and analytics workflows comply with safety and efficacy requirements for clinical use. This step also defines whether a product qualifies as a medical device under local or international law.
Finally, a biomarker must function outside controlled trials. Operational validation tests scalability: Can it handle data diversity, device interoperability, and cross-population differences? Real-world performance confirms that the digital biomarker remains stable and interpretable even as user behavior, devices, and environments vary.
Together, these four pillars create a framework for turning experimental data signals into clinically valid biomarkers. Without all four, even the most innovative algorithm risks being dismissed as “interesting but unproven.”
Clinical validation doesn’t end with testing; it must align with established medical data standards and regulatory frameworks that ensure safety, interoperability, and reproducibility. For digital biomarkers, these standards bridge the gap between innovative technology and clinical credibility.
To achieve clinical acceptance, digital biomarkers and their underlying systems must comply with internationally recognized standards:
Compliance with these frameworks establishes a foundation of trust, traceability, and safety, turning digital biomarkers into certifiable medical-grade measures. For more information on ISO Certification, check our blog post!
For digital biomarkers to function seamlessly across devices and systems, they must adhere to recognized data interoperability standards:
Together, these standards ensure that digital biomarkers are not isolated metrics but part of an interconnected clinical ecosystem where data flows securely and meaningfully between patients, providers, and researchers.
Even the most sophisticated biomarkers require skilled oversight. Certified health data analysts, biostatisticians, and regulatory affairs specialists ensure that digital biomarkers are interpreted correctly and used responsibly in clinical settings. Certification programs like the Certified Health Data Analyst (CHDA) by AHIMA emphasize competence in data governance, analytics, and compliance, essential skills for any team working with regulated digital biomarkers.
In short, clinical validation is both a scientific and procedural journey. It’s not only about proving a biomarker’s accuracy but also demonstrating that it operates within a robust, auditable framework trusted by clinicians, regulators, and patients alike.
While digital biomarkers promise to revolutionize healthcare, their journey to clinical validity remains complex. Several challenges stand in the way of widespread adoption and standardization:
Ultimately, digital health innovators must balance the pace of innovation with scientific rigor, ensuring that speed doesn’t come at the expense of safety, reliability, or equity.
A biomarker’s value lies not just in its ability to collect data, but in how reliably that data reflects real physiological processes and clinical outcomes. Achieving that standard requires collaboration between engineers, clinicians, and regulators, aligning technical precision with scientific rigor and patient safety.
Validated digital biomarkers don’t just make better products; they build trust among users, healthcare providers, and regulatory bodies. They enable faster approvals, stronger clinical partnerships, and truly evidence-based innovation.
At Thryve, we’re committed to supporting product teams and researchers on this journey, providing a harmonized certified API that meets the highest privacy and accuracy standards.
If you’re developing digital biomarkers or integrating wearable data into clinical programs, now is the time to prioritize validation.
Book a demo with Thryve to explore how our platform can help you build, test, and scale clinically reliable digital biomarkers with confidence.
Paul Burggraf, co-founder and Chief Science Officer at Thryve, is the brain behind all health analytics at Thryve and drives our research partnerships with the German government and leading healthcare institutions. As an economical engineer turned strategy consultant, prior to Thryve, he built the foundational forecasting models for multi-billion investments of big utilities using complex system dynamics. Besides applying model analytics and analytical research to health sensors, he’s a guest lecturer at the Zurich University of Applied Sciences in the Life Science Master „Modelling of Complex Systems“
