Science Blog

Wearable technologies and their associated informatics platforms gather, store, and process vast amounts of health-related, real-world data. These datasets can be some of the most complex used in health research. If the end-goal is to provide evidence in regulatory decision making, implementing well-defined practices to demonstrate sufficient data quality and fidelity is a must.

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.

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 first guiding principle to the development and discovery of novel digital biomarkers is hypothesis-driven research. In this article, we will describe our approach and provide examples of how we use this principle to ensure robust digital biomarker development.

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.

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.

San Diego State University’s Clinical Psychology research group and the Drexel University WELL Center joined forces using VivoSense^® to investigate Heart Rate Variability (HRV) and its link to obesity, binge eating, and loss of control while eating. Specifically, the study examined the association between HRV and binge eating and change in HRV from a resting to a stressed task as a potential marker of emotional regulation capacity in obese individuals.

Here’s an example of how a clinical research team studying stuttering respiratory patterns and other biosignals used VivoSense® software to integrate a multitude of wearable physiological sensor data, manage signal artifacts and produce robust data analysis.

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.