This is an open label iterative study designed to calibrate the algorithm to maximize its test characteristics. The sample size is derived from the minimum number of COVID-19 subjects required to evaluate algorithm sensitivity and specificity. A sample size of 200 true positive COVID-19 infections is anticipated to assess device predictive analytics. Assuming a six-month enrollment period, a 15 percent patient lost to follow-up rate, and a positive COVID-19 infection rate of 10 percent within the study population, a sample size of 2,352 subjects will be enrolled in this prospective study.~Primary Outcomes: The primary outcomes of interest are performance of the BioSticker multiparameter vital signs wearable and the sensitivity of the BioCloud-based algorithms in detecting early onset of active COVID-19 related signs and symptom in subjects prior to or coincident with a positive COVID-19 diagnostic test and the specificity of the BioSticker algorithms in distinguishing positive diagnosis of infection from negative diagnosis of infection among subjects. COVID-19 infection will be confirmed by both participant affirmation and diagnostic laboratory testing. Additional primary outcome is the intuitive presentation of affirmative diagnostic results from the BioSticker early detection algorithm(s).~Secondary Outcomes: The secondary outcomes of interest include correlation of biometric data indicators to individual-level experiential feedback reported through participant symptom-tracking and diagnostic questionnaires.
The goals of this study are 1) to validate the use of a wearable diagnostic capability and software as a medical device (SaMD) algorithm for the pre or early-symptomatic detection of COVID-19 infection, 2) assess the wearable device on the subjects, and 3) ensure data are collected, securely stored, and easily read and interpreted by non-laboratory personnel.