mHealth & Wearables
Remote monitoring and leveraging wearable devices and sensors in clinical trials.
Bring Your Own Device (BYOD) in Clinical Trials
BYOD promises greater patient-centricity by enabling patients to conduct assessments using the convenience and familiarity of their own hardware devices.
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 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.
The Path To A Successful Digital Trial
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.
Transforming Clinical Trials: The Path To A Successful Digital Trial
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
- Infographic: ICON supporting research into Alzheimer’s Disease
- Can actigraphy outcome measures from existing clinical trials provide a framework for future?
- Video: The Trouble with Alzheimer’s: Transforming Treatment, Prevention and Care
Useful resources on the use of wearables, sensors and remote monitoring:
- Remote monitoring and trial conduct in a care home setting
- Webinar: Activity Monitoring, Setting Standards for Clinical Research.
- Leveraging smartphone potential for clinical trials: accessing inbuilt sensors and components to measure health outcomes
- Real-Time Monitoring of the Digital Patient in Clinical Trials
- Applications for Wearables and Apps in Precision Medicine
- Using machine learning to detect specific motor movements
- Leveraging motion-based gaming platforms to measure health outcomes
Case Studies
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?
-
The Use of Digital Technologies to Collect Patient Data in Outcomes Research
Standards for measuring activity in COPD and other less active populations
Recommendations on standard approaches for wearable selection, implementation, and derived endpoints to measure changes in activity.
Sedentary behaviour is an important risk factor for a number of chronic diseases
Learn how to measure treatment related changes in sedentary behaviour using wearable technology.