The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review

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.

Figure 1

Comparison of the sensitivity and specificity of different machine learning models used for early SARS-CoV-2 detection The size of the circle representing each study is proportional to its number of participants. The colour of the circle is proportional to the percentage of participants positive for SARS-CoV-2 in the study.

Figure 2

An overview of the main physiological parameters analysed across different studies The SARS-CoV-2 associated changes in physiological parameters are shown with upward triangles (indicating a value increase), downward triangles (indicating a value decrease), and circles (indicating parameters were analysed in the study but direction of change was not reported). Notably, Bogu and Snyder's algorithm found bidirectional heart rate abnormalities compared with baseline measurements. Similarly, Natarajan and colleagues report an overall increase in heart rate variability due to COVID-19, despite an initial decrease.