Evidence map›Paper›PMID 38510705›Full record

ReviewBiophysics reviews2023

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.

Huijie Li, Jianhe Yuan, Gavin Fennell, Vagif Abdulla, Ravi Nistala, Dima Dandachi, Dominic K C Ho, Yi Zhang

Abstract readReview
In one paragraph

Review in Biophysics reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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.

Huijie LiDepartment of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA.ORCID https://orcid.org/0000-0002-8304-5241
Jianhe YuanDepartment of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA.ORCID https://orcid.org/0000-0002-4004-6236
Gavin FennellDepartment of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA.ORCID https://orcid.org/0000-0002-2963-0052
Vagif AbdullaDepartment of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA.ORCID https://orcid.org/0000-0001-5262-2950
Ravi NistalaDivision of Nephrology, Department of Medicine, University of Missouri-Columbia, Columbia, Missouri 65212, USA.ORCID https://orcid.org/0000-0001-9577-8074
Dima DandachiDivision of Infectious Diseases, Department of Medicine, University of Missouri-Columbia, 1 Hospital Drive, Columbia, Missouri 65212, USA.
Dominic K C HoDepartment of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, Missouri 65211, USA.ORCID https://orcid.org/0000-0002-4883-2151
Yi ZhangDepartment of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, USA.ORCID https://orcid.org/0000-0002-0907-663X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Identifiers

PMID38510705
PMCPMC10903389

What OpenQuestion holds

Textmetadata
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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.