mHealth & Wearables
Remote monitoring and leveraging wearable devices and sensors in clinical trials
Advancing digital endpoints
Navigating the shift from traditional trial models to agile, patient-centric processes driven by digital health technologies.
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.
Whitepaper: Wearables and digital endpoint strategy and validation
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.
- Develop a strategy to identify devices that are "fit for purpose"
- The ICON framework for Digital Endpoint selection and validation to ensure the outcome measurement is robust, reliable, and interpretable
- Address the key considerations that arise when using digital technology to support endpoint generation in clinical studies such as Device Selection, Endpoint Reliability and Sensitivity, Meaningful Change Thresholds, and Analysis Strategy and Interpretation
- Use our checklists for device selection and data strategy
Wearables webinar recordings
Digital endpoint strategy and validation
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.
Best practices for implementing a successful digital trial
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.
eCOA - insights into equivalence testing methodology and implications for BYOD
Smartphone sensors to measure novel health outcomes
Activity monitoring, setting standards for clinical research
Blogs and articles on wearables, sensors and remote monitoring:
- Blog: Wearables across phases: How to best incorporate digital endpoints at every stage of clinical research
- Article: The potential of activity trackers to bias study results
- Blog: Post pandemic clinical trial strategies
- Blog: Wearables and digital endpoint generation
- Article: The digital trial: How technology is changing trial design, start-up and close-out
- Article: Adopting mHealth technologies in cardiovascular trials (PDF)
- Article: Wearables in virtual clinical trials
- Blog: Key considerations for achieving digital trial success
- Blog: Remote monitoring and trial conduct in a care home setting
-
Blog: Real-time monitoring of the digital patient in clinical trials
- Blog: Applications for wearables and apps in precision medicine
- Blog: Using machine learning to detect specific motor movements
- Blog: Leveraging motion-based gaming platforms to measure health outcomes
- Blog: Objective measurement of sedentary behaviour using accelerometers
- Blog: Measuring activity in COPD and other less active populations
The value of measuring sleep and activity in Alzheimer's disease trials.
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.
- Using objective measures of activity and standardisation of endpoints to clarify the value and impact of exercise in patients with Alzheimer’s disease
- The Journal of Prevention of Alzheimer’s Disease - The case for using actigraphy generated sleep and activity endpoints in Alzheimer’s disease clinical trials
- Can actigraphy outcome measures from existing clinical trials provide a framework for future?
Case studies: wearables in clinical trials
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.
Leveraging BYOD, wearables and shareables in Real World Evidence studies
Approaches to leveraging mobile, wearable and shareable technology in observational research
- Webinar: What's Next in eCOA? - New insights into equivalence testing methodology and implications for BYOD
- Podcast: How Bring-Your-Own-Device (BYOD) is changing clinical research
-
Article: Scrip Bring Your Own Device ePRO: Hold the relish, or no holds barred?
-
Article: The use of digital technologies to collect patient data in outcomes research
-
White paper: On a technology-enabled collision course - clinical research meets clinical practice through Real World Evidence