Remote monitoring and leveraging wearable devices and sensors in clinical trials
BYOD promises greater patient-centricity by enabling patients to conduct assessments using the convenience and familiarity of their own hardware devices.
Incorporating Digital Health technologies into clinical trial designs has the potential to address many clinical trial challenges, including patient retention and engagement.
Wearable devices and sensors offer great potential in the collection of richer data and insights to enhance our understanding of the effects of treatment. They enable the collection of objective measures of intervention effects both in-clinic and in remote free-living settings. However, implementing wearables and sensors brings new challenges to clinical trial conduct, data management, including digital endpoint validation, and interpretation.
Our ICON Insights will help you to understand and successfully address the complexities of implementation of wearable devices in trial design, execution and reporting.
Although mHealth devices and sensors are continuing to evolve, and it is now possible to capture a vast array of physiological data, the operationalization of digital trial is not without challenges. Published in mid-June, pre-register to receive our Digital Endpoints whitepaper:
mHealth device technology has evolved to the point where it is now possible to collect a vast array of physiological data, sleep and activity data, and using advanced analytics to monitor patients in their own home outside of the hospital environment.
In a jointly authored article, ICON and Intel explore industry concerns about implementation of this technology in a clinical trial, including Patient Acceptance, Device Suitability, Data Complexity and Insight Generation, Operationalisation, Privacy and Security Issues, and Regulatory Acceptance.
Sleep quality and quantity have clinical relevance in Alzheimer's disease. Review the use of wearables in Alzheimer’s disease to provide objective measures of sleep and activity patterns that are not subject to patient recall bias.
Management wearables and data in a global trials
ICON’s eCOA team and wearables consultants design, implement and manage a technology solution for a global trial across nine countries amongst patients suffering from a neurological disorder
Developing and validating endpoints derived from wearables data
ICON uses AI machine learning algorithms to develop new digital biomarkers from raw accelerometer data.
Leveraging technology to conduct a study in a decentralised care setting
ICON uses Apple Research Kit to deliver an electronic patient reported outcomes instrument using an iPad and a wrist wearable, amongst elderly patients.
Approaches to leveraging mobile, wearable and shareable technology in observational research
Recommendations on standard approaches for wearable selection, implementation, and derived endpoints to measure changes in activity.
Learn how to measure treatment related changes in sedentary behaviour using wearable technology.