ABSTRACT
Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0.52-0.92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.
ABSTRACT
Objectives: This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows:. The primary objective is to determine the diagnostic accuracy of chest imaging (computed tomography (CT), chest X-ray and ultrasound) in the evaluation of people suspected to have COVID-19. This may also refer to specific signs, such as a particular CT finding. Secondary objectives To evaluate whether these imaging tests are sufficiently accurate to rule out COVID-19 (main measure of interest will be the negative predictive value) To evaluate the rate of positive imaging in patients with initial RT-PCR negative results who have a positive result on a follow-up RT-PCR test To determine if there is an association between number of days after symptom onset, symptom severity and the findings on chest imaging for patients with COVID-19 To determine the rate of discrepancy or agreement between CT, chest X-ray and ultrasound findings To evaluate for ‘threshold’ effects of imaging findings of COVID-19 and accuracy measures To determine the rate of alternative diagnoses identified by chest imaging.