Nowadays, anyone can use any of the many pre-made wearable devices to keep track of their health, but not all of them are created equal.

One Harvard biostatistician recently discovered that while devices like the Apple Watch are fine for RPM Devices Theme vitals like heart rate in the homes and lives of the average Joe who is concerned about his or her health, they are frequently unsuitable for clinical research.

The open-source data platform Beiwe was developed by JP Onnela, associate professor of biostatistics at the Harvard T.H. Chan School of Public Health. Onnela was considering utilizing the Apple Watch in a collaboration with the department of neurosurgery at Brigham and Women's Hospital; however, he discovered that the heart rate variability data collected from the devices contained numerous inconsistencies. The fact that the data had been filtered through software rather than being raw made the problem even worse.

Due to their lack of transparency, these algorithms are referred to as "black boxes." Decentralizing clinical research with technology is difficult because it requires raw, unfiltered data.

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Real-world data device manufacturer Vivalink collaborates with academic institutions to provide medical sensors and data analysis assistance as a means of addressing the issue of black boxes. The sensors are designed to capture continuous vital signs, are patient-friendly, and reusable. Most importantly, they can supply clinical research with raw data.

Sam Liu, vice president of Vivalink, spoke with Clinical Trials Arena about research-specific monitoring devices, their suitability for continuous real-world data capture in remote settings, and some of the intriguing trials using them.

Parkins, Kezia: Why are wearables that are available for purchase typically not suitable for clinical research?
Liu, Sam: Data can be collected in a variety of ways, especially through the use of technologies for remote patient monitoring (RPM). However, prior to being presented to the application or the clinician, the data collected by some devices may be subjected to filters.

The majority of popular off-the-shelf wearables are known as "black boxes." They are all self-contained because the formulas, filters, and algorithms used to get the data are already built into the device. The manufacturers deliberately don't show this information because it can be confusing, and most people don't need it. However, for research, your data may require greater fidelity or granularity.

KP: How have you recently improved your platform for real-world evidence biometric data?
SL: While real-world data can be collected continuously or intermittently by sensors, what happens if a network is lost or the wearer is far from a mobile device that will send the data to the cloud? Onboard memory has been added to some of our devices, allowing the sensors to continue collecting data about you and storing it on the sensor itself even if there is no network connection, Bluetooth, or Wi-Fi, when you wear them. It automatically synchronizes data when the device is back in range with a mobile device or gateway that can send data to the cloud. This is crucial for continuously gathering real-world data in a typical remote setting.

KP: How do you offer your customers assistance with data analysis?
SL: We offer sophisticated algorithms in addition to raw data as well as processed or smoothed-out data. For instance, we have algorithms that are able to identify more than 15 distinct kinds of arrhythmias. We provide that as part of our service because not every clinical research provider will have their own software for processing the data. In order to process data in such a way that it not only provides data but also more of a clinical insight into a particular disease state, we are looking at other types of more advanced software and algorithms.

KP: What kinds of tests are your devices participating in?
SL: Our ongoing trial at The University of California, San Francisco is very interesting because it is one of the largest of its kind and involves 3,000 participants using a continuous ECG monitor like ours. The trial essentially aims to identify biomarkers that provide early warning signs of atrial fibrillation (AFib), the most common arrhythmia that results in a wide range of problems, including an increased risk of stroke. Currently, treatment is more symptom-based. The goal of this study is to see if we can identify an earlier onset of AFib in order to develop a treatment strategy.

We need a lot of ECG data, something that can continuously capture the patient's data over a long period of time in real-world situations, to conduct such a large study. This is a three-year study that could last up to ten years. Patients are required to wear our patch for approximately a week each month, according to the protocol. Our technology's distinctive design and high-quality features make it ideal for that. Our sensors are very small, comfortable, and won't bother you. It can be worn without being noticeable underneath your clothing. To really guarantee that the patient will continue to use it for a significant amount of time, it is well-suited to the patient's way of life.
Additionally, our ECG device was utilized in a Stanford University study on teenage depression, a condition that is extremely prevalent and unfortunately occasionally results in suicide. Because there isn't just one thing that can cause depression, it's a complicated condition. Although it is difficult to comprehend stress itself, we do know that stress plays a significant role in depression. Different aspects of life cause people stress. Some people are easily overwhelmed by a straightforward issue, whereas others are overwhelmed by more complex ones. The main goal of the study at the University of STANFORD was to measure heart rate variability, which is a physiological indicator of your body's stress level.

Now, some stress is normal and not all stress is bad. When we work, exercise, or attempt to resolve a problem, we experience constant stress. But stress can sometimes be very bad for you. We can measure that activity throughout the day using an electrocardiogram (ECG) to determine your physiological stress level. It's possible that you have a more serious issue at hand if your stress peaks persist over time.

Stanford used our technologies to measure that because you can't see a trend in how long the peaks last without a lot of data over a long time.

KP: When the Covid-19 pandemic began, how did Vivalink monitor patients in hospitals?
SL: Since our devices are made for remote patient monitoring (RPM), we were surprised to find that hospitals and clinics are interested in using the same technology. We started receiving a lot of international inquiries when Covid-19 came out. While our healthcare system in the United States is extremely technologically advanced, not all hospitals in other parts of the world have that kind of money. In poorer nations, hospitals are pretty primitive, and doctors and nurses run from bed to bed to provide and monitor healthcare.

Because there is a significant risk of exposure with Covid, hospitals in remote areas investigated the utilization of wearable RPM technology to remotely monitor patients from a different ward of the same hospital without being infected.

Our wearables were initially popular in China, where they were utilized in approximately fifteen different hospitals.

In India, we recently completed a substantial transaction, and in Vietnam, we are currently working on another. We currently have business partners in approximately 25 nations. Knowing that these RPM technologies not only solve problems but also save money when compared to conventional, costly hospital equipment is reassuring. In many developing nations, a single piece of furniture for a single bed in a single room cannot cost $5,000. The price is insane when multiplied by one hundred. The costs of wearables are significantly lower than those of conventional equipment, often only one-tenth or one-fifth as much.

KP: How do you see wearables in clinical research in the future?
SL: Because healthcare is largely centralized, which means that the population seeks treatment in a few central locations, I believe that wearables are altering the healthcare paradigm. Most of the time, the patient receives very basic and basic care. As a result, I believe you can deliver better care to remote regions of the world using RPM. In essence, we are reducing technology's size and cost so that it can be used in more places around the world. I believe that has the greatest impact.