{"references": ["Swan M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data. 2013 Jun;1(2):85\u201399.", "Haghi M, Thurow K, Stoll R. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthc Inform Res. 2017 Jan;23(1):4\u201315.", "Islam MM, Mahmud S, Muhammad LJ, Islam MR, Nooruddin S, Ayon SI. Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19). SN Comput Sci. 2020 Oct 1;1(6):320.", "Carr E, Bendayan R, Bean D, Stammers M, Wang W, Zhang H, et al. Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study. BMC Med. 2021 Jan 21;19(1):23.", "Miller DJ, Capodilupo JV, Lastella M, Sargent C, Roach GD, Lee VH, et al. Analyzing changes in respira-tory rate to predict the risk of COVID-19 infection. medRxiv [Internet]. 2020; Available from: https://www.medrxiv.org/content/10.1101/2020.06.18.20131417v2.abstract", "Jeong H, Rogers JA, Xu S. Continuous on-body sensing for the COVID-19 pandemic: Gaps and oppor-tunities. Sci Adv [Internet]. 2020 Sep;6(36). Available from: http://dx.doi.org/10.1126/sciadv.abd4794", "Varga Z, Flammer AJ, Steiger P, Haberecker M, Andermatt R, Zinkernagel AS, et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet. 2020 May 2;395(10234):1417\u20138.", "Semeraro F, Scquizzato T, Scapigliati A, Ristagno G, Gamberini L, Tartaglione M, et al. New Early Warning Score: off-label approach for Covid-19 outbreak patient deterioration in the community. Resuscitation. 2020 Jun;151:24\u20135.", "Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. The Lancet Digi-tal Health. 2020 Feb 1;2(2):e85\u201393."]}

The COVID-19 pandemic has led more people to start using wearable technology to track vital signs, physical activity, and sleep. The significant features of these devices include their capability to collect continuous, noninvasive data. We developed a COVID-19 risk stratification model using the Biostrap wearable device which utilizes a baseline-adjusted continuous scale and other escalation points-based on our recent case report, to enhance the National Early Warning Score (NEWS2). Preliminary research has found that our adjusted Early Warning Score (Biostrap-EWS) might be highly specific in identifying early-stage respiratory infections. We present the case of Biostrap CEO Sameer Sontakey, a 35-year-old man, whom the app notified as having a high likelihood of respiratory illness after which the diagnosis SARS-CoV-2 was confirmed with a nasal swab. Our Biostrap-EWS algorithm appears to detect respiratory infections in a real-world environment via passively collected biometric data. To validate the reliability of the algorithm, further research is required.