MEDICAL DEVICES


Collecting trial data outside the black box


Wearable devices and remote sensors are key to unlocking decentralised clinical trials, but not all tech is created equal.


T


hese days, anyone can track their health with the myriad off-the-shelf wearable devices available, but not all wearables


are created equal.


While devices such as the Apple Watch will do fine in tracing and monitoring vitals sch as heart rates in the homes and lives of the health-conscious average Joes, they are often not suitable for clinical research, as one Harvard biostatistician recently found out. JP Onnela, associate professor of biostatistics at the Harvard TH Chan School of Public Health and the developer of open-source data platform Beiwe, was considering using the Apple Watch in a collaboration with the department of neurosurgery at Brigham and Women’s Hospital, but he discovered the heart rate variability data collected from the devices was riddled with inconsistencies. The issue was exacerbated by the fact that the data wasn’t raw bt filtered throgh software. These algorithms are known as black boxes


for their lack of transparency and represent a significant hrdle in decentralising clinical research with tech, which requires raw, nfiltered data. As a solution to the issue of black boxes,


realworld data device firm ivalin wors with research institutions to provide medical sensors and data analysis support. The sensors are reusable, patient-friendly and optimised for continuous vitals capture. Most importantly, they can provide raw data for the purpose of clinical research. linical Trials Arena sat down with ivalin


vice-president Sam Liu to discuss research- specific monitoring devices, why they are suited to continuous real-world data capture in remote settings and some of the


interesting trials making use of them.


Kezia Parkins: Why are commercially available wearables not usually suitable for clinical research?


Sam Liu: There’s a number of different ways that data can be collected, especially using remote patient monitoring (RPM) technologies. However, with some devices, there may be filters applied to the data when it’s collected before it is presented to the application or the clinician. Most of the off-the-shelf wearables


that the public are familiar with tend to be black box. They’re all self-contained – the formlas, filters and algorithms to derive the data are already built into the device, and the manufacturers purposely don’t expose it because most people don’t need to know that information as it can be confusing. For research though, your data may need a higher level of granlarity or fidelity.


KP: How did you recently enhance your real-world evidence biometric data platform?


SL: Sensors can collect real-world data continuously or episodically, but what happens when there’s a network disconnection or the wearer is not near a mobile device that’s going to transmit the data to the cloud? One of the enhancements we have made in


some of our devices is onboard memory, so that when you’re wearing our sensors, even if you have no network connection, Bluetooth or Wi-Fi, the sensors will still continue to collect data about you and store it on the


Outsourcing In Clinical Trials | 49


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