Video

Using Smart Bed Technology to Approximate Influenza-Like Illness

Author(s):

Dr. Garcia-Molina speaks to how smart bed technology can unobtrusively collect data to predict and track the development of symptoms associated with respiratory illnesses.

A new study being presented this week at the 2022 World Sleep Congress in Rome, Italy, will offer insights into new smart bed technology from Sleep Number that approximates influenza-like illness (ILI) rates through sleep and cardiorespiratory data.

Previous Sleep Number studies have leveraged sleep metrics from smart bed technology to create a COVID-19 symptom detection model. The current investigation evaluated whether the COVID-19 prediction could be used to detect ILI symptoms by comparing pre-pandemic smart bed sleep data to ILI trend reports by the US Centers for Disease for Disease Control and Prevention (CDC).

Data from the new study showed a correlation of 0.91 between ILI symptoms predicted with the Sleep Number model and CDC-reported rates, and coefficients close to 1.0 indicate a positive correlation.

Additionally, when analyzing the 2018-19 influenza season, the correlation of predicted and reported ILI rates was 0.87.

In an interview with HCPLive, Gary Garcia-Molina, PhD, Senior Principal Scientists at Sleep Number, spoke of how the new ILI smart bed model unobtrusively collects data to predict seasonal trends in ILI rates, and how smart bed technology has influenced sleep studies in recent years.

In previous decades, sleep science relied on a practice called polysomnography, a comprehensive test used to diagnose sleep disorders that required patients to be tested in a lab. While in the lab, patients would be evaluated based on brain signals, muscle movements, respiratory activity and more.

Over time, investigators observed that sleep data collected from these lab studies were markedly different from the data collected from home-based studies, as some patients sleep patterns changed depending on the location.

With new smart bed technology, Garcia-Molina noted that sleep data could now be accurately and unobtrusively collected without even leaving the house.

“The great advantage of the system we have is that it captures sleep in a very naturalistic foundation - you don't need to go to the lab, you can do it at home,” Garcia-Molina said. “The second (advantage) is that it looks at the changes in respiratory and cardiac activity as measured by a sensor that doesn't touch you. It’s not a sensor that is affixed to your body, or you have to put electrodes on our body, nothing like that.”

He added that the aim of smart bed technology has been to establish a “massive” database of ecologically valid asleep data over long periods of time to see how sleep patterns inform common disorders in addition to cardiovascular and cognitive disorders.

Garcia-Molina was confident that new developments in smart bed technology would encourage patients with sleep disorder sot play a more active role in their physical, mental, and emotional health.

“I really foresee a future in we can go to the to the doctor with our data and share the data with the healthcare practitioners and basically be active actors in preserving our wellness and our health,” Garcia-Molina said. “I think that Sleep Number, in that regard, is taking the appropriate strategy and helping with that effort.”

To hear more from Garcia-Molina on the newest study from Sleep Number, watch the video above.

Related Videos
Rahul N. Khurana, MD: Phase 1 Results on Vamikibart for Uveitic Macular Edema | Image Credit: Northern California Retina Vitreous Associates
Sunir J. Garg, MD: | Image Credit: Wills Eye Hospital
James Del Rosso, DO: Discussing What’s New in the Medicine Chest for Dermatologists
What to Look Forward To at the Fall Clinical Dermatology Conference, with Raj Chovatiya, MD, PhD
Christine N. Kay, MD: Interim Data on ATSN-201 Shows Promise for XLRS | Image Credit: Vitreo Retinal Associates
Arshad Khanani, MD: First Results from Fellow Eye Dosing of RGX-314 in nAMD | Image Credit: Sierra Eye Associates
Jonathan Barratt, MD | Credit: IgA Nephropathy Foundation
How Artificial Intelligence is Being Used in Lung Imaging, with Rachel Eddy, PhD
Joel A. Pearlman, MD, PhD: Phase 2a Data on Oral RZ402 for DME | Image Credit: Retina Consultants Medical Group
© 2024 MJH Life Sciences

All rights reserved.