Evidence map›Paper›PMID 34713021›Full record

ReviewFrontiers in digital health2020

Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments.

Dhruv R Seshadri, Evan V Davies, Ethan R Harlow, Jeffrey J Hsu, Shanina C Knighton, Timothy A Walker, James E Voos, Colin K Drummond

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in digital health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 5 pooled it
21.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

112 citing papers in PubMed, 5 syntheses or guidelines pooled it, 315 citations in OpenAlex.

  1. Pooled it
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  5. Pooled it
  6. Effect of digital tools to promote hospital quality and safety on adverse events after discharge.Journal of the American Medical Informatics Association : JAMIA · 2024
    Trial
  7. Article
  8. Review
  9. Article
  10. Article
  11. Wearable sensors in paediatric neurology.Developmental medicine and child neurology · 2025
    Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
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  20. Utilisation of the web-based Home Assessment Tool among patients with COVID-19 in Selangor, Malaysia: An observational study.Malaysian family physician : the official journal of the Academy of Family Physicians of Malaysia · 2024
    Article

52 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Dhruv R SeshadriDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Evan V DaviesDepartment of Electrical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Ethan R HarlowDepartment of Orthopaedics, University Hospitals of Cleveland Medical Center, Cleveland, OH, United States.
Jeffrey J HsuDivision of Cardiology, Department of Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States.
Shanina C KnightonDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Timothy A WalkerDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
James E VoosDepartment of Orthopaedics, University Hospitals of Cleveland Medical Center, Cleveland, OH, United States.
Colin K DrummondDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Case Western Reserve University · USUniversity Hospitals of Cleveland · USUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has brought into sharp focus the need to harness and leverage our digital infrastructure for remote patient monitoring. As current viral tests and vaccines are slow to emerge, we see a need for more robust disease detection and monitoring of individual and population health, which could be aided by wearable sensors. While the utility of this technology has been used to correlate physiological metrics to daily living and human performance, the translation of such technology toward predicting the incidence of COVID-19 remains a necessity. When used in conjunction with predictive platforms, users of wearable devices could be alerted when changes in their metrics match those associated with COVID-19. Anonymous data localized to regions such as neighborhoods or zip codes could provide public health officials and researchers a valuable tool to track and mitigate the spread of the virus, particularly during a second wave. Identifiable data, for example remote monitoring of cohorts (family, businesses, and facilities) associated with individuals diagnosed with COVID-19, can provide valuable data such as acceleration of transmission and symptom onset. This manuscript describes clinically relevant physiological metrics which can be measured from commercial devices today and highlights their role in tracking the health, stability, and recovery of COVID-19+ individuals and front-line workers. Our goal disseminating from this paper is to initiate a call to action among front-line workers and engineers toward developing digital health platforms for monitoring and managing this pandemic.

Indexed as

COVID-19pandemicpredictive analyticsremote patient monitoringwearable sensors

Identifiers

PMID34713021
PMCPMC8521919
OpenAlexW3037676165

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.