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. 2022 Sep:162:107170.
doi: 10.1016/j.ypmed.2022.107170. Epub 2022 Jul 22.

Wearable technology for early detection of COVID-19: A systematic scoping review

Shing Hui Reina Cheong  1 , Yu Jie Xavia Ng  2 , Ying Lau  3 , Siew Tiang Lau  4
Affiliations

Wearable technology for early detection of COVID-19: A systematic scoping review

Shing Hui Reina Cheong et al. Prev Med. 2022 Sep.

Abstract

Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.

Keywords: Artificial intelligence; COVID-19; Early detection; Wearable technology.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
PRISMA 2020 flow diagram of study retrieval and selection process. Note: ECG = electrocardiography and PPG = Photoplethysmography.
Fig. 2
Fig. 2
Types of wearable technology used for early detection of COVID-19.
Fig. 3
Fig. 3
Frequency of wearable technology types amongst 40 included articles.
Fig. 4
Fig. 4
Wearable technology mechanism: Anomaly detection model developmental process.
See this image and copyright information in PMC

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