Yvonne Chan, MD, PHD, FACEP, Director, Center for Digital Health, Associate Professor of Genetics and Genomic Sciences and Associate Professor of Emergency Medicine at Icahn School of Medicine at Mount Sinai, explores how digital health is revolutionizing clinical trials. This is part of our series on clinical data collection and management.
At the Mount Sinai Center for Digital Health (CDH), we believe mobile technology as well as connected devices could enable valuable multi-dimensional, granular, real-world clinical data capture. In 2015, the CDH team led the pioneering application of Apple’s ResearchKit framework to enable a large scale prospective observational study of asthma, with over 10,000 research participants from three countries. The study helped demonstrate the power and utility of citizen science and that smartphones could become the ubiquitous tool that brings clinical research to the masses.
We found that the mobile platform may increase participant diversity by facilitating the inclusion of traditionally underrepresented and difficult to recruit populations, (eg those with disabling, severe disease, living outside academic center catchment areas etc) by removing some barriers that has historically crippled study enrollment and ongoing participation. Beyond the unprecedented scaling of recruitment - over 3000 participants enrolled in three days post-study launch - the breadth and depth of data collected, such as surveys, devices, geolocation and air quality, were also remarkable.
Given the trend towards digital communication methods, some traditional research methodologies may yield biased data and results in the near future. For example, responses to oral and written surveys that are conducted by phone or mail, may represent a more restricted and non-representative population. Given every approach has its own inherent benefits and shortcomings, the participant’s personal circumstances and preferences will determine the acceptability of these approaches. Therefore, offering and leveraging the strength of both digital health and traditional research methods, whenever feasible, could optimize clinical study enrollment, participant retention, and data capture than either method alone.
Benefits to both researchers and patients
Smartphone-based technology and digital health (DH) technologies in general, could provide innovative, scalable solutions for clinical research aspirations that were logistically not feasible or cost-prohibitive in the past. Researchers and stakeholders running trials could benefit from the automation and standardization of certain mundane, time-consuming aspects of the process, while still retaining the ‘human touch’ at carefully selected key touch points of the study. This ‘human in the loop’ strategy has been shown to improve study retention and decrease costs.
Additionally, mobile health technology can provide the participant continual, near real-time feedback on status/progress, push notifications, reminders and other useful engagement features. This capability is one important advantage of smartphones because it provides researchers with a way to interact with participants more regularly and frequently than previously possible. This capability can be fine-tuned and leveraged to become a powerful tool in behavioral modification.
Furthermore, DH allows clinicians and researchers to obtain a more holistic view of an individual or a population’s health. The traditional trial methodology of examining isolated variables or relationships in a near vacuum frequently doesn’t yield the most useful, generalizable results, given the extraordinary messiness and complexity of human diseases and behaviors. The concurrent development of sophisticated analytical methods, including machine learning/AI, can help us make sense of the big data generated by the DH/Internet of Things/genomics era. The goal is to identify and predict important signals or patterns associated with disease or wellness for each individual so that we can offer personalized prevention and treatment strategies.
Explore other posts from our clinical data series here.