In the early adoption phase, the mHealth technology was almost a distraction, and was the focus of the study rather than the clinical value, or patients themselves. There was a view among some in the industry that one could purchase a number of smartwatches, hand them out to subjects, and generate data that could be used to support a clinical endpoint.
Today the market has matured. There is a wide range of medical grade sensors and devices that can be used to capture a myriad of biometric data such as oximetry, heart rate, respiratory rate, and galvanic skin response. It is self-evident that a number of strategies need to be implemented in order to ensure the device selected is fit for purpose; has low patient burden, and generates the required clinical data. A number of industry led initiatives such as that led by ePRO Consortium, are striving to provide guidance around the evidence required to support the use of devices in a clinical trial. Other initiatives are ongoing, focusing on standards and the approaches to identify the main barriers to implementation and ensure more mainstream use of devices and sensors in trials. There is still some concern regarding the response of the regulatory authorities, however the FDA have been proactive in response to mHealth devices and have set up a specific website dealing with connected medical devices, mobile apps, and software and have issued a number of guidance documents dealing with digital health.

As we move beyond the initial hype into the maximising value of the data we face new challenges. With the increasing availability of connected devices, we have the ability to create composite endpoints by combining data from multiple devices with subjective insights from ePRO. As we transition from a snapshot in clinic data capture to real-time continuous data capture, data storage and management needs increase exponentially. The data storage requirements for a traditional 200 patient study is estimated to be in the megabytes range compared to terabytes for a study that captures sub-second accelerometer data. Beyond the storage requirements, data needs to be readily accessed and analysed, ensuring data privacy and security compliance are in line with local legal requirements such as EU General Data Protection Regulation (GDPR). This presents new challenges and opportunities for those involved in global studies using mHealth technologies. Digital platforms such as Validic, Intel, and The Hyve facilitate the scaling of data capture and aggregation from multiple sensors. Similarly, cloud based platforms such as the Amazon Web Services (AWS) and Microsoft Azure are refining their services so that in addition to the storage and database options they are now offering machine learning and analytics tools.

It is clear that wearables and sensors are seen as key components in facilitating new trial design. This approach is gaining momentum in clinical trials design. However, the true value of wearables and sensors will emerge in the coming decade as their use is moving beyond mere hype to creating patient-centric trials and the earlier availability of new therapies improving the lives of patients.