Remote monitoring and leveraging wearable devices and sensors in clinical trials
Navigating the shift from traditional trial models to agile, patient-centric processes driven by digital health technologies.
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.
The COVID-19 pandemic has heightened interest in mHealth and mobile technology to capture patient insights outside of the traditional clinical setting. The application of COA principles and techniques can be applied to build the evidence package, when considering the evidence required to support digital endpoints for their submission to regulatory bodies and reimbursement stakeholders.
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.
Wearables are improving the clinical trial experience for patients and satisfying the need to collect data for real-world use studies.
Developing a protocol that incorporates data collection via mHealth technologies requires a comprehensive solution that has a strong patient engagement element and a robust digital framework.
To understand these large datasets, AI and machine learning are necessary to automate analyses.
Wearables are purpose-built to engage users, with optimized interfaces, easy-to-use screens, appealing companion apps and easy-to-interpret dashboards.
New ways to collect data and transforming clinical operations.
Maximising AI’s involvement in pharma development is to utilise its ability to draw distinctions and correlations, which would otherwise elude human observation.
The foundation of healthcare is shifting from a provider-based to a patient-centric, or value-based, model.
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.
ICON’s wearables and patient outcomes teams were able to objectively measure cough using patient-centric, novel technologies.
Generating respiratory disease specific biomarkers using advanced analytics.
ICON uses AI machine learning algorithms to develop new digital biomarkers from raw accelerometer data.
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.
Approaches to leveraging mobile, wearable and shareable technology in observational research
How to best incorporate digital endpoints at every stage of clinical research?
How COVID-19 altered clinical trials forever and what’s next?
An end-to-end approach to managing wearable devices through clinical development.
An end-to-end solution is required to run a successful digital trial.
The REACHES study considered how assessments that are traditionally conducted at a clinic visit.
Direct to patient strategies and crafting patient centric trials are of increasing interest in drug development trials.
Patients increasingly act like consumers, expecting to control decisions about their care and to receive individualised products and services
Accelerometers can capture significant quantities of raw data, potentially containing patterns
which, could quantify specific motor movements.
ICON’s Innovation lab have begun to develop prototype solutions that could be used in clinic to make objective assessments of movement and mobility.
Measuring treatment-related changes in sedentary behaviour using wearable technology.
While activity monitors have been used in clinical trials, some researchers consider a number of perceived barriers limiting their use.
How to manage data from wearables in clinical trials — from overcoming regulatory issues to handling challenges associated with dirty data.
The more that can be done to encourage patients to participate in clinical trials, the faster new medicines and devices become available.