Using Smart Bed Technology to Approximate Influenza-Like Illness

March 14, 2022
Armand Butera

Armand Butera is the assistant editor for HCPLive. He attended Fairleigh Dickinson University and graduated with a degree in communications with a concentration in journalism. Prior to graduating, Armand worked as the editor-in-chief of his college newspaper and a radio host for WFDU. He went on to work as a copywriter, freelancer, and human resources assistant before joining HCPLive. In his spare time, he enjoys reading, writing, traveling with his companion and spinning vinyl records. Email him at abutera@mjhlifesciences.com.

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.


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