www.thelancet.com/digital-health Vol 4 May 2022 e370 Review Lancet Digit Health 2022; 4: e370–83 *Joint first authors Julius Global Health (M Mitratza PhD, A Shagadatova MSc, D Veen PhD, Prof D E Grobbee PhD, G S Downward PhD), Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands (Prof J van de Wijgert PhD, P Stolk PhD); Ava AG, Zurich, Switzerland (B M Goodale PhD, V Kovacevic PhD, M Cronin PhD); Julius Clinical Research BV, Zeist, Netherlands (T B Brakenhoff PhD, B Franks PhD, D Veen, Prof D E Grobbee); Institute of Health Informatics, University College London, London, UK (Prof R Dobson PhD, A A Folarin PhD); Optentia Research Program, North-West University, Potchefstroom, South Africa (D Veen); National Institute for Health Research Maudsley Biomedical Research Centre, King’s College London, London, UK (A A Folarin); Department of Biostatistics and Health Informatics, South London and Maudsley NHS Foundation Trust, London, UK (A A Folarin) Correspondence to: Dr Marianna Mitratza, Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht 3508GA, Netherlands m.mitratza@umcutrecht.nl The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review Marianna Mitratza*, Brianna Mae Goodale*, Aizhan Shagadatova, Vladimir Kovacevic, Janneke van de Wijgert, Timo B Brakenhoff, Richard Dobson, Billy Franks, Duco Veen, Amos A Folarin, Pieter Stolk, Diederick E Grobbee, Maureen Cronin, 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. Introduction On Dec 31, 2019, WHO recognised the emergence of SARS-CoV-2, a novel virus in the coronavirus family.1 Since then, the outbreak of illness caused by the SARS-CoV-2 virus (COVID-19) has become a global pandemic, causing more than 458 million cases and 6 million deaths, until March, 2022.2 A key strategy for containing the COVID-19 pandemic has been the rapid identification and contact tracing of inf