In recent years, wearable devices that track sleep, activity, heart rate, and recovery through smartwatches, fitness bands, and rings have been increasingly used by millions of people.
Wearables can correlate to smaller studies, shorter timelines, more patient-centred outcomes, and potentially show stronger evidence of treatment benefit. However, within the pharmaceutical industry, where objective, continuous patient data could transform research and drug development, wearable technology remains surprisingly underutilised in clinical trials.
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Digital measures change the economics of a clinical trial, said Dudley Tabakin, founder and CEO of VivoSense, at the Outsourcing in Clinical Trials (OCT) UK & Ireland 2026 conference, which took place on June 9–10 in London, UK.
Digital measures derived by wearables can lead to more efficient and commercially successful trials by reducing the size of the patient groups, study length, and hence costs, said Tabakin. Newport Coast, California-based VivoSense is a wearable sensor data analytics company that specializes in transforming complicated and high-volume data from wearables into meaningful digital biomarkers for clinical research.
On the other hand, traditional clinical trial endpoints suffer from a fundamental limitation—they capture snapshots of a patient’s condition through episodic patient visits. Tabakin supports that “digital measures move beyond episodic assessments.” For instance, patients may visit the clinical for the six-minute walk test (6MWT), once every few months, or fill in questionnaires about symptoms from memory, or undergo clinic-based assessments that fail to reflect their everyday experiences. A patient’s condition can fluctuate significantly between visits, and some symptoms may go unnoticed.
Wearable devices offer a different and promising model. Rather than episodic observations, they provide continuous measurements that are more reliable. Monitoring in real-world settings can capture small changes in function, feeling, and health that may present themselves in mobility, and even overall activity patterns, said Tabakin. This can be particularly significant for patients in rare disease clinical trials, where validated clinical endpoints are often lacking because they are not sensitive enough or miss rare and infrequent events typical to that indication.
The reality of implementation
Tabakin estimated that just over 1,000 clinical trials have incorporated wearable sensors over the past 25 years, which is a small fraction of the estimated trials conducted during the same period. Although wearable sensors are an opportunity for growth, Tabakin offered insight into why wearables remain underused in clinical research. “Wearables provide a high volume of data (multiple samples every second), and without strategy, they deliver noise and not insights,” he said.
Some of the limitations that are necessary to overcome are that data can be fragmented, incomplete, noisy or collected without sufficient oversight. Patients can forget to wear devices, algorithms might not be suitable for specific disease populations, and there can be site-level errors, patient noncompliance, and signal loss in a clinical trial, which are all challenges. Protocols designed for conventional clinical assessments often fail to account for the operational demands of continuous monitoring.
When implementing wearables into clinical research, wearable data quality depends as much on operational execution as on the technology itself. Incomplete data is typically unsalvageable, and there is a need to preserve data integrity and data quality through compliance monitoring and operational oversight to ensure that the statistical power of the study is not compromised. In addition to data integrity and quality, data availability is key, and according to Tabakin, 95% data availability can be achieved by interventions and strategies focusing on protocol-specific compliance monitoring and operational oversight.
Data interpretation complexities
Apart from data integrity and availability, data interpretation is of utmost significance. According to Tabakin, most commercial wearable devices and algorithms are built and validated based on healthy individuals, whereas clinical trials mostly focus on diseased populations, and thus data interpretations can differ significantly from those of healthy users. This is the “algorithm trap”, Tabakin said.
For example, sleep disruption is a common symptom of chronic obstructive pulmonary disease (COPD). The standard algorithm of the wearable device in a COPD trial overestimated sleep efficiency, which contradicted the patient’s clinical experience. In this instance, disease-specific algorithm adjustments should be made for researchers to observe sleep patterns consistent with COPD-related sleep disturbances, and the algorithm needs to be optimized for the COPD-specific population to accurately measure sleep efficiency, Tabakin said.
Unlocking the regulatory value of wearables
However, probably the biggest challenge to promoting wearable-derived data is regulatory validation and acceptance. Tabakin stated that, as of now, there are no digital endpoints that have successfully been the primary basis for approval of a new drug. Unlocking the full potential of digital measures is complicated and requires early regulator engagement and interaction.
Tabakin spoke about his experience of VivoSense identifying a digital measure that outperformed traditional clinical assessments for a neuromuscular disease program sponsor. The measure demonstrated clear treatment differentiation and was advanced to discussions with regulators and accepted by the regulators as a key secondary endpoint in a Phase III program, Tabakin explained. It is crucial to measure something that is meaningful to the patients rather than just established in-clinic measures. This is a requirement of regulatory acceptance.
However, success in identifying new meaningful digital endpoints needs early planning. Early protocol design, sensor selection, bespoke algorithm development, continuous operational monitoring, clinical validation, and early and ongoing engagement with regulators are critical steps, according to Tabakin. Overall, Tabakin highlighted that validation must occur on three levels: verification of the sensor itself, analytical validation of the measurement, and clinical validation demonstrating relevance to patient outcomes.
Such an implementation strategy holds the potential to enhance the reliability and robustness of digital data for research and also shifts research toward a more patient-centred direction, especially for individuals living with rare diseases or chronic conditions.
The implementation issue does not lie in whether wearable sensors can collect useful data. The real challenge is how researchers can transform digital data into robust evidence that can gain regulatory approval and accelerate research. If that challenge can be addressed, wearable devices could reshape not only how patients monitor their health, but also drug development.
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