*Joint first authors
Julius Global, PhD Marianna Mitratza, Shagadatova Msc, Prof Veen Phd, PhD, G S Downward PhD D E Grobbee, Prof Janneke Van De Wijgert, Diederick Pieter Stolk, PhD Brianna Mae Goodale, PhD Vladimir Kovacevic, PhD Maureen Cronin, PhD Timo B Brakenhoff, Franks PhD Duco Veen, Prof Richard Dobson, PhD Amos A Folarin, Aizhan Shagadatova, Billy Franks, E Grobbee, George S Downward
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.
Contributors All authors had full access to the data, including statistical reports and tables, in the study. MM, BMG, and GSD take responsibility for the integrity of the data and the accuracy of the data analysis. MM, BMG, DEG, PS, MC, and GSD contributed to the study concept and design. MM, BMG, AS, VK, and GSD contributed to the acquisition, analysis, and interpretation of data and provided administrative, technical, and material support. MM, BMG, and GSD contributed to drafting the manuscript. All authors contributed to a critical revision of the manuscript for important intellectual content. DEG, PS, and MC obtained funding. GSD was responsible for study supervision.
Declaration of interests MM, BMG, VK, BF, DV, DEG, MC, and GSD received grants from Innovative Medicines Initiative 2 Joint Undertaking (number 101005177), during the conduct of the study. BMG reports consulting fees and employment from Ava Science, support for attending meetings and travel from Ava Aktiengesellschaft (AG), a patent application from Ava AG (P24892CH00) filed with the Swiss Federal Institute of Intellectual Property for System and Method for Pre-Symptomatic and/or Asymptomatic Detection of a Human Viral or Bacterial Infection based on pilot data from the COVID-RED clinical study, and consultancy for Falcon Health and TheraB Medical, outside the submitted work. VK reports employment from Ava Science and Ava AG, during the conduct of this study. TBB, BF, DV, and DEG report employment from..
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