These days, anyone can track their health with the myriad off-the-shelf wearable devices available, but not all wearables are created equal.
While devices like the Apple Watch will do fine in tracking and monitoring vitals like heart rate in the homes and lives of the health-conscious average Joe, they are often not suitable for clinical research, as one Harvard biostatistician recently found.
JP Onnela, associate professor of biostatistics at the Harvard T.H. Chan School of Public Health and developer of the 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 discovered that the heart rate variability data collected from the devices were riddled with inconsistencies. The issue was exacerbated by the fact that the data wasn’t raw, but filtered through software.
These algorithms are known as ‘black boxes’ for their lack of transparency and represent a significant hurdle in decentralising clinical research with tech, which requires raw unfiltered data.
As a solution to the issue of black boxes, real-world data device firm Vivalink works 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 purposes of clinical research.
Clinical Trials Arena sat down with Vivalink vice president Sam Liu to discuss research-specific monitoring devices, why they are so suited for 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 formulas, 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 granularity 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 that 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 sensor itself. When the device is back in range with a mobile device or gateway that can then transmit data to the cloud, it automatically synchronises it. That’s so important for collecting continuous real-world data in your typical remote environment.
KP: How do you support your clients with data analysis?
SL: In addition to providing data, both the raw and the processed or smoothed out data we also provide advanced algorithms. We have algorithms that can detect over 15 different arrhythmia types for example. Not every clinical research provider will have their own software to process the data so we provide that as part of our service. We’re looking at other types of more advanced algorithms and software to process data so that it presents not just data, but more of a clinical insight into a certain disease state.
KP: What are some of the trials in which your devices are being used?
SL: We have a trial ongoing at The University of California, San Francisco which is very interesting as it’s one of the largest of its type using a continuous ECG monitor like ours in a population of 3,000 participants. The purpose of the trial is to basically identify biomarkers that give you early warning signs of atrial fibrillation (AFib) which is the most common arrhythmia problem and causes lots of issues including increased levels of stroke. The way it’s treated today is more symptom-based. This study is to see if we can get an earlier indication of the onset of AFib to try to conduct some kind of preventive medicine.
To conduct such a large study like this, we need a lot of ECG data – something that can continuously capture the patient’s data in real-world situations over a long period of time. It’s an up to 10-year study that’s now in its third year. The protocol calls for patients to wear our patch for about a week at a time, every month on an ongoing basis. The unique design of our technology is very well suited for that and so are the qualitative features. Our sensors are very small, non-intrusive and comfortable. You can wear it and you won’t even notice it under your clothing. It fits well into the lifestyle to really ensure that the patient will continue to use it over a long period of time.
Our ECG device was also used in a University of Stanford study in teenage depression, something that is very common and unfortunately sometimes leads to suicide. Depression is a complex thing as there’s not just one thing that causes depression. What we do know is that stress is a big part of depression but stress itself is complex to understand. People are stressed by different factors in their life. Some people are easily stressed by a simple issue while it takes others more complex issues to stress them out. The study at the University of STANFORD was to basically measure heart rate variability, which is a physiological indicator of the level of stress your body is undergoing.
Now, not all stress is bad – a lot of stress is normal. We’re stressed every day when we work, when we exercise or when we’re trying to solve a problem. But, sometimes, stress can be very detrimental. With an ECG we can measure that activity throughout the day to see where your physiological stress is at. If stress peaks are maintained on an ongoing basis, that can be an indication that you have a bigger problem.
Stanford used our technologies to measure that because you need to have data over a long period of time so you can see the trend of how long the peaks last.
KP: How did Vivalink conduct in-hospital patient monitoring at the beginning of the Covid-19 pandemic?
SL: Our devices are designed for remote patient monitoring (RPM) so we were surprised that there has been a lot of interest in using the same technology inside hospitals and clinics. When Covid-19 came around, we started getting a lot of international inquiries. In the US, we have a very technologically advanced healthcare system but not all hospitals in other parts of the world have that kind of money. In poorer countries, hospitals are pretty basic and healthcare is provided and monitored the old-fashioned way with doctors and nurses running from bed to bed.
With Covid, there is a lot of risk of exposure so hospitals in remote areas looked to use wearable RPM technology to remotely monitor patients from a different ward from the same hospital without being exposed to the infection.
Interest initially started off in China where our wearables got deployed to about 15 different hospitals.
We just closed a pretty large deal four months ago in India and we’ve got another one going on in Vietnam. We have commercial partners in about 25 countries now. It’s comforting to know that these RPM technologies are not only solving problems but they’re also cost-effective compared to traditional expensive hospital equipment. Often in a developing country, you can’t spend $5,000 on one piece of equipment for one bed in one room. Multiply that by 100 and the cost is crazy. With wearables, the costs are much less – easily one-fifth or one-tenth the cost of traditional equipment.
KP: What do you see for the future of wearables in clinical research?
SL: I think wearables are changing the paradigm of healthcare because healthcare is largely centralised, meaning the population goes to some central locations to get care. When the care has to go to the patient it tends to be very basic and rudimentary. So with RPM, I think you can take a higher quality of care to dispersed parts of the world. We’re basically miniaturising technology, both in terms of size and also cost, so that it becomes far more usable throughout the world. I think that’s the biggest impact.