Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters, discernible by wearable devices, could act as early digital biomarkers of infections.
Summary
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, 2022.2A key strategy for containing the COVID-19 pandemic has been the rapid identification and contact tracing of infected individuals.3, 4RT-PCR constitutes the gold standard for diagnostic testing of COVID-19.5, 6, 7 Despite developments in rapid testing, the timing of testing in relation to stage of infection hinders public health efforts to control the virus.8 On average, from SARS-CoV-2 infection to symptom onset takes 6 days, although the incubation period can be as long as 18 days.9 The viral load from the upper respiratory tract increases during the incubation period, reaches a peak around symptom onset, and then gradually declines.10 Many national health guidelines recommend testing for the general population after symptom onset, or a few days after suspected exposure to the virus, regardless of symptoms, to limit false-negative test results.11, 12, 13, 14 However, viral load could be sufficiently high enough for transmission before people have symptoms or qualify for testing.15, 16COVID-19 remains difficult to distinguish from other respiratory illnesses on the basis of reported symptoms alone. Many common COVID-19 symptoms (eg, fever and cough) overlap with other influenza-like illnesses.17, 18 Some patients with confirmed COVID-19 report symptoms uniquely associated with the virus (eg, anosmia), but such symptoms rarely appear early in the disease.19Furthermore, 20–30% of individuals infected with SARS-CoV-2 never develop symptoms.20, 21, 22 The US Centers for Disease Control and Prevention report that presymptomatic or asymptomatic people account for half of SARS-CoV-2 virus transmissions.23To reduce transmission rates in the general population, identifying SARS-CoV-2 infections before or in the absence of symptom onset is crucial. A range of non-invasive, commercially available physiological monitors (ie, wearable devices) could help in detecting presymptomatic and asymptomatic infections and controlling the pandemic. Because of rapid technological advancements, relatively subtle fluctuations in physiological parameters such as body temperature, respiratory rate, heart rate, heart rate variability, skin perfusion, and oxygen saturation (SpO2) can be measured by sensors commonly found in smartwatches, smart rings, and fitness trackers. Fever remains one of the most commonly reported COVID-19 infection symptoms;24 thus, the inclusion of thermometer sensors on an increasing number of wearable devices, despite their reliance on sensors worn on distal body parts, might render them suitable to detecting SARS-CoV-2 infection. Of note, peripheral temperatures measured by wearable devices have shown greater sensitivity than oral measurements in detecting subtle temperature shifts (eg, ≥0·2°C).25 With regard to the COVID-19 pandemic, wrist temperatures have been found to be equally stable and less susceptible to environmental influences than forehead temperatures.26 Calls for additional research on the role wearable devices could serve in the early and comprehensive detection of SARS-CoV-2 infections have emphasised their potential ability to inform population and individual health responses to the pandemic.27 Several studies, mostly of retrospective design, have shown the feasibility of wearable devices in indicating the presence of SARS-CoV-2 infection by monitoring one or more physiological parameters, but an overview of the evidence is not yet available. In this systematic review, we aimed to summarise and assess the added value of wearable devices in the detection of SARS-CoV-2 infection within the adult population (ie, those 18 years and older). Our primary question regards the current state of evidence on the diagnostic accuracy of statistical and algorithmic models using wearable sensor data. We also consider the time from detection to symptom onset and which physiological parameters provide the best indication of a subclinical or symptomatic SARS-CoV-2 infection.
Results
The first database search, done on Dec 17–21, 2020, identified 1601 records with an additional four articles retrieved from manually screening review reference lists. The second search, conducted on March 8–9, 2021, found an additional 574 records, and the third search, done on July 27, 2021, found an additional 1691 records, resulting in 3196 unique records overall, after deduplication. After title and abstract screening, 173 articles were retained for full-text review, of which 12, 19, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 fulfilled our inclusion and exclusion criteria (appendix p 7). All studies were observational, and seven were strictly retrospective;19, 30, 31, 32, 35, 38, 40 although some researchers implemented control procedures, no RCTs were reported. Our searches also identified 12 study protocols, including two RCTs.Eight protocols were recorded in online registries; one was a preprint, and three were published (appendix pp 8–9). During extraction, we contacted the corresponding authors for studies with missing data and received replies from six of the 12 research teams. We compiled the key characteristics for the 12 studies included in this systematic review (table 1; see appendix pp 11–14 for a detailed description). Most studies recruited active users of wearable devices with a self-reported retrospective SARS-CoV-2 infection; none of the studies tested participants for the presence of SARS-CoV-2 antibodies to detect mild or asymptomatic infections for which the participant had not sought diagnostic testing. Researchers commonly used historical information from long-term wearable use to examine changes in physiological parameters in the days before and after a patient's diagnosis or symptom onset. The studies recruited predominantly from European and North American countries. Nine studies examined SARS-CoV-2 infection among the general public, whereas three enrolled health-care professionals.31, 36, 37 Three research teams characterised their studies as proof-of-concept studies.38, 39, 40 Four studies were pre-prints.31, 32, 36, 40
References
1. WHO
Novel coronavirus (2019-nCoV) situation report-1. https://apps.who.int/iris/bitstream/handle/10665/330760/nCoVsitrep21Jan2020-eng.pdf?sequence=3&isAllowed=yDate: Jan 21, 2020 Date accessed: December 3, 2021 View in Article
2. WHO
WHO coronavirus (COVID-19) dashboard. https://covid19.who.int/Date: 2020 Date accessed: March 16, 2022 View in Article
3. Kucharski AJ, Klepac P, Conlan AJK, et al.
Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study. Lancet Infect Dis. 2020; 20: 1151-1160View in Article
4. Kretzschmar ME, Rozhnova G, Bootsma MCJ, van Boven M, van de Wijgert JHHM, Bonten MJM
Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. Lancet Public Health. 2020; 5: e452-e459View in Article
5. WHO
Laboratory testing for coronavirus disease (COVID-19) in suspected human cases: interim guidance. https://apps.who.int/iris/handle/10665/331501Date: March 19, 2020 Date accessed: March 16, 2022 View in Article
6. Corman VM, Landt O, Kaiser M, et al.
Detection of 2019 -nCoV by RT-PCR. Euro Surveill. 2020; 25: 1-8View in Article
7. US Food and Drug Administration
Coronavirus testing basics. https://www.fda.gov/media/138094/downloadDate: May, 2020 Date accessed: December 3, 2021 View in Article
8. Dinnes J, Deeks JJ, Adriano A, et al.
Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst Rev. 2020; 8CD013705View in Article
9. Elias C, Sekri A, Leblanc P, Cucherat M, Vanhems P
The incubation period of COVID-19: a meta-analysis. Int J Infect Dis. 2021; 104: 708-710View in Article
10. Walsh KA, Jordan K, Clyne B, et al.
SARS-CoV-2 detection, viral load and infectivity over the course of an infection. J Infect. 2020; 81: 357-371View in Article
11. European Centre for Disease Prevention and Control
COVID-19 testing strategies and objectives. https://www.ecdc.europa.eu/sites/default/files/documents/TestingStrategy_Objective-Sept-2020.pdfDate: Sept 15, 2020 Date accessed: December 3, 2021 (3rd).View in Article
12. Centers for Disease Control and Prevention
COVID-19 testing: what you need to know. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.htmlDate: 2021 Date accessed: March 23, 2021 View in Article
13. National Institute for Public Health and the Environment
Testing for COVID-19. https://www.rivm.nl/en/novel-coronavirus-covid-19/testing-for-covid-19Date: 2021 Date accessed: March 23, 2021 View in Article
14. National Health Service
Testing for coronavirus (COVID-19). https://www.nhs.uk/conditions/coronavirus-covid-19/testing/Date: 2021 Date accessed: March 23, 2021 View in Article
15. Zou L, Ruan F, Huang M, et al.
SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med. 2020; 382: 1177-1179View in Article
16. He X, Lau EHY, Wu P, et al.
Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020; 26: 672-675View in Article
17. Allen WE, Altae-Tran H, Briggs J, et al.
Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing. Nat Hum Behav. 2020; 4: 972-982View in Article
18. Centers for Disease Control and Prevention
Similarities and differences between flu and COVID-19. https://www.cdc.gov/flu/symptoms/flu-vs-covid19.htmDate: 2021 Date accessed: December 3, 2021 View in Article
19. Shapiro A, Marinsek N, Clay I, et al.
Characterising COVID-19 and influenza illnesses in the real world via person-generated health data. Patterns (N Y). 2020; 2100188View in Article
20. Ing AJ, Cocks C, Green JP
COVID-19: in the footsteps of Ernest Shackleton. Thorax. 2020; 75: 693-694View in Article
21. Byambasuren O, Cardona, Bell K, Clark J, McLaws ML, Glasziou P
Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. J Assoc Med Microbiol Infect Dis Canada. 2020; 5: 223-234View in Article
22. Buitrago-Garcia D, Egli-Gany D, Counotte MJ, et al.
Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: a living systematic review and meta-analysis. PLoS Med. 2020; 17e1003346View in Article
23. Johansson MA, Quandelacy TM, Kada S, et al.
SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Netw Open. 2021; 4e2035057View in Article
24. WHO
Report of the WHO–China joint mission on coronavirus disease 2019 (COVID-19). https://www.who.int/publications-detail/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19)Date: Feb 28, 2020 Date accessed: December 3, 2021 View in Article
25. Zhu TY, Rothenbühler M, Hamvas G, et al.
The accuracy of wrist skin temperature in detecting ovulation compared to basal body temperature: prospective comparative diagnostic accuracy study. J Med Internet Res. 2021; 23e20710View in Article
26. Chen G, Xie J, Dai G, et al.
Validity of the use of wrist and forehead temperatures in screening the general population for covid-19: a prospective real-world study. Iran J Public Health. 2020; 49: 57-66View in Article
27. Seshadri DR, Davies EV, Harlow ER, et al.
Wearable sensors for COVID-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Front Digit Health. 2020; 2: 8View in Article
28. Mitratza M, Goodale BM, Downward GS, Stolk P, Shagadatova A
Performance of wearable sensors in the detection of COVID-19: a systematic review. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021232910Date: 2021 Date accessed: March 16, 2022 View in Article
29. National Heart, Lung, and Blood Institute
Study quality assessment tool for observational cohort and cross-sectional studies. https://www.nhlbi.nih.gov/health-pro/guidelines/in-develop/cardiovascular-risk-reduction/tools/cohortDate: 2014 Date accessed: March 23, 2021 View in Article
30. Miller DJ, Capodilupo JV, Lastella M, et al.
Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. PLoS One. 2020; 15e0243683View in Article
31. Cleary JL, Fang Y, Sen S, Wu Z
A caveat to using wearable sensor data for COVID-19 detection: the role of behavioral change after receipt of test results. medRxiv. 2021; (published online April 22.) (preprint).https://www.medrxiv.org/content/10.1101/2021.04.17.21255513v1 View in Article
32. Nestor B, Hunter J, Kainkaryam R, et al.
Dear watch, should I get a COVID-19 test? Designing deployable machine learning for wearables. medRxiv. 2021; (published online May 17.) (preprint).https://www.medrxiv.org/content/10.1101/2021.05.11.21257052v1 View in Article
33. Mishra T, Wang M, Metwally AA, et al.
Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng. 2020; 4: 1208-1220View in Article
34. Quer G, Radin JM, Gadaleta M, et al.
Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med. 2021; 27: 73-77View in Article
35. Natarajan A, Su HW, Heneghan C
Assessment of physiological signs associated with COVID-19 measured using wearable devices. NPJ Digit Med. 2020; 3: 156View in Article
36. Hassantabar S, Stefano N, Ghanakota V, et al.
CovidDeep: SARS-CoV-2/COVID-19 test based on wearable medical sensors and efficient neural networks. arXiv. 2020; (published online Oct 28.) (preprint).https://arxiv.org/abs/2007.10497 View in Article
37. Hirten RP, Danieletto M, Tomalin L, et al.
Use of physiological data from a wearable device to identify SARS-CoV-2 infection and symptoms and predict COVID-19 diagnosis: observational study. J Med Internet Res. 2021; 23e26107View in Article
38. Smarr BL, Aschbacher K, Fisher SM, et al.
Feasibility of continuous fever monitoring using wearable devices. Sci Rep. 2020; 1021640View in Article
39. Lonini L, Shawen N, Botonis O, et al.
Rapid screening of physiological changes associated with COVID-19 using soft-wearables and structured activities: a pilot study. IEEE J Transl Eng Health Med. 2021; 94900311View in Article
40. Bogu GK, Snyder MP
Deep learning-based detection of COVID-19 using wearables data. medRxiv. 2021; (published online Jan 9.) (preprint).https://www.medrxiv.org/content/10.1101/2021.01.08.21249474v1 View in Article
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