Wearables can track COVID symptoms, other diseases
Date:
April 19, 2022
Source:
University of Michigan
Summary:
If you become ill with COVID-19, your smartwatch can track the
progression of your symptoms, and could even show how sick you
become.
FULL STORY ==========================================================================
If you become ill with COVID-19, your smartwatch can track the progression
of your symptoms, and could even show how sick you become.
========================================================================== That's according to a University of Michigan study that examined the
effects of COVID-19 with six factors derived from heart rate data. The
same method could be used to detect other diseases such as influenza,
and the researchers say the approach could be used to track disease at
home or when medical resources are scarce, such as during a pandemic or
in developing countries. Their results are published in the journal Cell Reports Medicine.
Following U-M students and medical interns throughout the country, the researchers discovered new signals embedded in heart rate indicating
when individuals were infected with COVID and how sick they became. The researchers found that individuals with COVID experienced an increase
in heart rate per step after symptom onset, and those with a cough had
a much higher heart rate per step than those without a cough.
"We found that COVID dampened biological timekeeping signals, changed
how your heart rate responds to activity, altered basal heart rate and
caused stress signals," said Daniel Forger, professor of mathematics and research professor of computational medicine and bioinformatics. "What we realized was knowledge of physiology, how the body works and mathematics
can help us get more information from these wearables." The researchers
found that these measures were significantly altered and could show
symptomatic vs. healthy periods in the wearers' lives.
"There's been some previous work on understanding disease through wearable heart rate data, but I think we really take a different approach by
focusing on decomposing the heart rate signal into multiple different components to take a multidimensional view of heart rate," said Caleb
Mayer, a doctoral student in mathematics.
==========================================================================
"All of these components are based on different physiological
systems. This really gives us additional information about disease
progression and understanding how disease impacts these different
physiological systems over time." Participants were drawn from the 2019
and 2020 cohorts of the Intern Health Study, a multisite cohort study
that follows physicians across several institutes in their first year of residency. Researchers also used information from the Roadmap College
Student Data Set, a study that examined student health and well-being
during the 2020-21 academic year using wearable data from Fitbits, self-reported COVID-19 diagnoses and symptom information, and publicly available data.
For this analysis, the researchers included individuals who reported a
COVID- positive test, symptoms and had wearable data from 50 days before symptom onset to 14 days after. In all, the researchers used data from
43 medical interns and 72 undergraduate and graduate students.
Specifically, the researchers found:
* Heart rate increase per step, a measure of cardiopulmonary
dysfunction,
increased after symptom onset.
* Heart rate per step was significantly higher in participants
who reported
a cough.
* Circadian phase uncertainty, the body's inability to time daily
events,
increased around COVID symptom onset. Because this measure relates
to the strength and consistency of the circadian component of the
heart rate rhythm, this uncertainty may correspond to early signs
of infection.
* Daily basal heart rate tended to increase on or before symptom
onset. The
researchers hypothesize this was because of fever or heightened
anxiety.
* Heart rate tended to be more correlated around symptom onset,
which could
indicate the effects of the stress-related hormone adenosine.
The researchers used an algorithm that was originally developed
to estimate daily circadian phase from wearable heart rate and step
data. They looked at a baseline period of 8-35 days before COVID symptom
onset and an analysis period defined as 7-14 days around COVID symptom
onset. The researchers hope that with further testing, the same methods
could enhance the pre-detection of COVID with wearables.
==========================================================================
"The global outbreak of the SARS-CoV-2 virus imposed important public
health measures, which impacted our daily lives," said Sung Won Choi,
associate professor of pediatrics. "However, during this historical
event in time, mobile technology offered enormous capabilities -- the
ability to monitor and collect physiological data longitudinally from individuals noninvasively and remotely.
"We were amazed at the U-M students' willingness and desire to participate
in this study, which was all done remotely, from recruitment to enrollment
and onboarding. The work reported by Mayer and our team was really
made possible not only through wearables sensors themselves, but the convergence of novel data analytics, remarkable advances in technology and computing power, and 'team science' intersection across research teams."
This "team science" approach coalesced as a side product of the 2019
U-M Ideas lab, which included the team's senior investigators.
The researchers say this work establishes algorithms that can be used to understand illnesses' impact on heart rate physiology, which can form
the basis for medical professionals might deploy the use of wearables
in health care.
"Identifying the varying patterns of different heart rate parameters
derived from wearables across the course of COVID-19 infection is
a substantial advance for the field," said Srijan Sen, professor of
psychiatry and director of the Frances and Kenneth Eisenberg and Family Depression Center at U-M. "This work can help us more meaningfully
follow populations in future COVID-19 waves. The study also demonstrates following cohorts with mobile technology and robust data sharing can
facilitate unanticipated and valuable discoveries." Limitations for the
study include that the work does not consider influenza- like illnesses, according to the researchers. Future work should focus on whether
the findings reflect the effects of COVID-19 or whether these effects
will persist in other illnesses. The researchers were also not able to
account for the effects of factors such as age, gender or BMI, nor the seasonality effects in the data -- that is, whether the data was taken
during a period of time where flu or other disease transmission is high.
Co-authors of the study also include U-M researchers Jonathan Tyler,
Yu Fang, Christopher Flora, Elena Frank and Muneesh Tewari. The work was supported by the National Institutes of Health, Human Frontier Science
Program, National Science Foundation and a Taubman Institute Innovation
Project grant.
========================================================================== Story Source: Materials provided by University_of_Michigan. Note:
Content may be edited for style and length.
========================================================================== Journal Reference:
1. Caleb Mayer, Jonathan Tyler, Yu Fang, Christopher Flora, Elena
Frank,
Muneesh Tewari, Sung Won Choi, Srijan Sen, Daniel
B. Forger. Consumer- grade wearables identify changes
in multiple physiological systems during COVID-19 disease
progression. Cell Reports Medicine, 2022; 3 (4): 100601 DOI:
10.1016/j.xcrm.2022.100601 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/04/220419112413.htm
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