Regulatory, safety, and privacy concerns of home monitoring technologies during COVID-19

Gerke, Sara; Shachar, Carmel; Chai, Peter R; Cohen, I. Glenn

doi:10.1038/s41591-020-0994-1

Cited by 110 publications

(94 citation statements)

References 8 publications

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“…Mass surveillance encourages users to understand that these strategies help the authorities to prevent possible social problems ( Arya et al., 2019 ), as the analysis of these data is an opportunity to predict new incidents, such as terrorist attacks, murders, collapses of the system, traffic jams, and so on ( Zuboff, 2015 ). For instance, while numerous previous studies found parallels with the measurement and tracking of coronavirus infections and future pandemics at a global level (e.g., Gerke et al., 2020 , Wen et al., 2020 )., this kind of tracking can violate privacy of users who use applications to track coronavirus infections.…”

Section: Introductionmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

162

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Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.

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Section: Introductionmentioning

confidence: 99%

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Ribeiro‐Navarrete

Saura

Palacios‐Marqués

2021

Technological Forecasting and Social Change

162

View full text Add to dashboard Cite

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“…Participants also completed a common symptom-check (reporting on fever, cough, subjective loss of taste and smell, etc), and reported on any formal COVID-19 testing they had undergone. Participants could generate a user-name to track their own performance over time, but the tool was otherwise completely anonymous to protect user privacy 10 .…”

Section: Resultsmentioning

confidence: 99%

A Novel Olfactory Self-Test Effectively Screens for COVID-19

Snitz

Honigstein

Weissgross

et al. 2021

Preprint

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Key to curtailing the COVID-19 pandemic are wide-scale testing strategies 1,2 . An ideal test is one that would not rely on transporting, distributing, and collecting physical specimens. Given the olfactory impairment associated with COVID-19 3-7 , we developed a novel measure of olfactory perception that relies on smelling household odorants and rating them online. We tested the performance of this real-time tool in 12,020 participants from 134 countries who provided 171,500 perceptual ratings of 60 different household odorants. We observed that olfactory ratings were indicative of COVID-19 status in a country, significantly correlating with national infection rates over time. More importantly, we observed remarkable indicative power at the individual level (90% sensitivity and 80% specificity). Critically, olfactory testing remained highly effective in participants with COVID-19 but without symptoms, and in participants with symptoms but without COVID-19. In this, the current odorant-based olfactory test stands apart from symptom-checkers (including olfactory symptom-checkers) 3 , and even from antigen tests 8 , to potentially provide a first line of screening that can help halt disease progression at the population level.

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“…Nonetheless, patients with severe or critical COVID-19 are most likely triaged for high dependency units or intensive care units are not the target candidates for remote monitoring. Last but not least, while technically attractive, potential privacy concerns may arise with sensitive physiological data from individual patients being transferred and stored in the networks, necessitating appropriate regulatory measures for proper privacy protect prior to large-scale implementationn 35 .…”

Section: Discussionmentioning

confidence: 99%

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

Wong

Lau

et al. 2021

Sci Rep

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Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.

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Regulatory, safety, and privacy concerns of home monitoring technologies during COVID-19

Cited by 110 publications

References 8 publications

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

Towards a new era of mass data collection: Assessing pandemic surveillance technologies to preserve user privacy

A Novel Olfactory Self-Test Effectively Screens for COVID-19

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

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