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

• Published in: Preventive medicine 2022-09, Vol.162, p.107170-107170, Article 107170
• Main Authors: Cheong, Shing Hui Reina, Ng, Yu Jie Xavia, Lau, Ying, Lau, Siew Tiang
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; COVID-19; COVID-19 - diagnosis; Early detection; Early Diagnosis; Humans; Research Design; Review; Wearable Electronic Devices; Wearable technology
• ISSN: 0091-7435; 1096-0260
• DOI: 10.1016/j.ypmed.2022.107170

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. •Smartwatch or fitness trackers were preferred types of wearable technology.•Medical devices had more diverse placements on the body.•Anomaly detection models utilised artificial intelligence or statistical analyses.•Detection models’ performances were overoptimistic due to shortcomings in model development.•Accuracy results were questionable due to the lack of validation standards.