Science Blog

As the leading provider of visualization and analysis software for data from wearable sensors, we are committed to continually improving VivoSense and expanding its capabilities for our academic research partners. Here are some of the highlights of our recent release of VivoSense^® 3.5.

The 3rd Annual Digital Biomarkers in Clinical Trials Summit hosted by Roche was the virtual place to be in June, bringing together 250+ global thought leaders and experts. They shared insights on developing and improving the way pharma clinical trials utilize mobile-enabled, wearables, and IoT capabilities to augment existing biomarker strategies, improve patient experience, and collect better data.

The study of human health in real-world, everyday environments is necessary to advance knowledge and discovery. Wearable sensors make this possible by providing customized body-worn monitoring and analysis solutions that are rooted in scientific foundations. Here’s an infographic highlighting the advantages using wearable technologies in your clinical trials.

Good data is more important than big data in the development of digital biomarkers. Big data is often sold as the solution to all digital data analyses and touted to revolutionize healthcare in the coming years. However, the problem with a superabundance of data is that digital biomarker development becomes a fishing expedition, and the catch may not be relevant to the question. Without a hypothesis (often the case in biomarker development with big data), accurate results may be too low to use in a clinical trial, or even worse, divergent with no findings.

Here’s an example of how a clinical research team used hypothesis-driven research methods to discover HRV (Heart Rate Variability) associations with stress disorder symptoms in Marines. VivoSense^® software was used to analyze the data collected, ensuring that unusable artifactual data was removed to allow for more accurate findings.

The future of clinical trials involves real-world data collected from wearable sensors and connected technologies. Pharmaceutical companies are investing heavily in digital innovation; however, the adoption of digital biomarkers remains slow. To overcome the challenges associated with high frequency, real-world data, and for digital solutions to realize their potential in clinical trials, we must develop robust and systematic approaches to their use.

The US and international effort to address the novel Coronavirus (COVID-19) pandemic is unprecedented. Experts from healthcare, public health, and policy are joining forces with experts in manufacturing and technology for the rapid deployment of innovative, lifesaving solutions. VivoSense, an expert in remote monitoring of physiological signals, is contributing to this effort through their advanced data analytics and Human Augmented AI platform.

Image Source: RSRT, Randol Family

VivoSense announces a series of new collaborations with the Rett Syndrome Research Trust (RSRT) to develop digital biomarkers that better assess the symptoms of Rett syndrome, focusing on autonomic dysfunction and other difficult-to-evaluate symptoms. The goal in developing these digital biomarkers is to better quantify and understand symptom impact on everyday life and to assess new treatments accurately.

The pace, pressures, work-life balance, and emotional aspects of a physician’s job put them at risk for burnout. A study by the American Medical Association and Mayo Clinic found that burnout rates were particularly increased for specialty practitioners. Drs. Nicholas Slamon and Rob Parker, pediatric ICU physicians at Nemours/Alfred I. duPont Hospital for Children in Wilmington, DE, used VivoSense^® and real-time biometrics of physician stress to study its effects on the care of pediatric patients.

Scientists are increasingly relying on wearable sensors and machine learning to develop digital biomarkers. However, their successful development requires the identification of physiological events relevant to the disease state. Here's an overview of the method we use to accurately and efficiently label physiological events from biosignals collected from wearable sensors.