Articles and Insights About Wearables for Clinical Trials

Wearable Data Analysis

How to Visualize Physiological Data for Clinical Trials Using VivoSense®

Posted by Dudley Tabakin on February 10, 2022

The best way to understand how VivoSense®️ software can help you visualize physiological data from your clinical trial is to see it in action. The first video below will take you through the graphical user interface, importing and opening a physiological data session, and exploring the data with the Data Explorer panel and the synchronized chart panel.

The second video will discuss some advanced tools, such as the channel chart and plot properties, create some save layouts for use with all data sessions, create annotation markings to identify events in the data, and export the data to ASCII.

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How Digital Data Impacts the Development of New Treatments

Posted by Patrick Hankey on June 24, 2021

Digitally connected devices are transforming the way we treat and manage health conditions. They also introduce and improve participant access, engagement, and outcome measurements in clinical trials. Here’s a look at how digital data is paving the way for advances in treatments and recovery.

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Creating a Data Quality System for Digital Biomarker Development

Posted by Kate Lyden on August 13, 2020

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.

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Good Data is Essential in Digital Biomarker Development

Posted by Dudley Tabakin on June 4, 2020

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.

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Analyzing Integrated Data From Multiple Physiological Sensors

Posted by VivoSense Team on January 30, 2020

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.

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How to Label Physiological Events to Discover Novel Digital Biomarkers

Posted by Dudley Tabakin on January 16, 2020

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.

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