Clinical trial NCT04548895: Non-invasive Biometric Monitoring in Nursing Homes to Fight COVID-19
Official title: Non-invasive Biometric Monitoring for the Prevention of COVID-19 Transmission and Deaths in Nursing Homes
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Covid19
Community-Acquired Respiratory Tract Infection
Keywords: COVID-19
wearables
biometric data
Lead sponsor: Health Stream Analytics, LLC
Sponsor class: INDUSTRY
First posted: 2020-09-16
Enrollment: 26
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: ECOLOGIC_OR_COMMUNITY
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Residents and staff members of U.S. LTCFs where COVID-19 transmission is actively occurring. The LTCF medical director must agree to enroll the LTCF, and each participant must have the capacity to agree and sign consent.

Brief summary: Solving the problem of detecting asymptomatic carriers who can transmit infection is key to protecting vulnerable residents of nursing homes and assisted living facilities, to protecting frontline workers who care for them, and to facilitating return to work (including return of nurses and medical assistants).

The wearable biometric technology, if widely disseminated among vulnerable populations and the community-at-large, will help avoid the ravages of seasonal flu and other contagious illnesses, and the society will be better prepared for future waves of COVID-19 or other pandemics. Even if a vaccine is developed, due to immune senescence and immunocompromise, elderly people and those with chronic medical conditions may not be well protected by it. Continuous biomonitoring provides another layer of protection for them.

Detailed description: 1. Building the algorithm for early, pre-symptomatic DETECTION OF RESPIRATORY VIRAL INFECTION and for predicting eventual DETERIORATION.
2. Create an APP that AUTOMATES these algorithms and clearly REPORTS ACTIONABLE RESULTS to users, i.e., to medical professionals and citizens-at-large in near-real time. If alerted to a possible - and likely still asymptomatic - COVID-19 infection, they can self-isolate or be quarantined, get confirmatory COVID-19 testing done promptly, limit transmission to others, and stay safe knowing that if they are likely to deteriorate, the algorithm will alert the participants and their caregivers to the need to obtain medical attention promptly.

Eligibility criteria: Inclusion Criteria:

* Residents and staff members of U.S. LTCFs where COVID-19 transmission is actively occurring. The LTCF medical director must agree to enroll the LTCF, and each participant must have the capacity to agree and sign consent.

Exclusion Criteria:

* Current atrial fibrillation.

  * NB: Paroxysmal atrial fibrillation is permitted if the participant is in atrial fibrillation less than 50% of the day on most days.
* Pacemaker in place.
* Known active infection other than COVID-19.
* Dementia

Primary outcomes:
measure: Proportion of quality signals obtained out of all monitoring time for each device
timeFrame: 8 weeks from first enrollment
measure: Predictive characteristics of the algorithm for respiratory tract infection
timeFrame: 2 months
Secondary outcomes:
Other outcomes:
Locations:
city: Pasco
country: United States
facility: Avalon Health & Rehabilitation Center
state: Washington
Study officials:
affiliation: Health Stream Analytics, LLC
name: Martin G Frasch
role: STUDY_CHAIR
References: