Evidence map›Paper›PMID 40680176›Full record

ArticleJMIR formative research2025

Remote Patient Monitoring for Global Emergencies: Case Study in Patients With COVID-19.

Ramin Ramezani, Wenhao Zhang, Minh Cao, Alex Bui, Antonia Petruse, Amelia Weldon, Arash Naeim

Abstract read
In one paragraph

Article in JMIR formative research, 2025. 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. Impact of the COVID-19 Pandemic on Buckle Fracture Treatment.Journal of the Pediatric Orthopaedic Society of North America · 2025
    Article
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

7 authors.

Ramin RamezaniDepartment of Computer Science, University of California, Los Angeles, 550, 404 Westwood Plaza, Engineering 6, Los Angeles, CA, 90095, United States, 1 4242997051.ORCID 0000-0001-9276-6932
Wenhao ZhangDepartment of Computer Science, University of California, Los Angeles, 550, 404 Westwood Plaza, Engineering 6, Los Angeles, CA, 90095, United States, 1 4242997051.ORCID 0000-0001-6053-6454
Minh CaoDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0001-5628-4864
Alex BuiClinical and Translational Science Institute (CTSI), University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-4702-1373
Antonia PetruseClinical and Translational Science Institute (CTSI), University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0003-2968-7750
Amelia WeldonClinical and Translational Science Institute (CTSI), University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-7187-574X
Arash NaeimClinical and Translational Science Institute (CTSI), University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0001-6830-5933

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The COVID-19 pandemic has highlighted the critical need for telehealth and remote patient monitoring in health care delivery. Despite the growing use of on-body wearable sensors for continuous monitoring and predicting adverse events, their widespread adoption remains a significant challenge. While the pandemic has accelerated the acceptance of these technologies, achieving widespread integration requires their sustained incorporation into routine health care practices beyond emergencies. In this study, we extend the application of our previously developed remote patient monitoring system to patients with COVID-19. Objective: Our objective is to assess whether the metrics obtained from our previously developed system can provide additional insights into the recovery trajectory of individuals affected by COVID-19. This case study aims to demonstrate that remote patient monitoring systems can be adapted to diverse patient cohorts during emergencies. We aim to illustrate the ease of deployment, particularly when these systems are already integrated into the existing health care ecosystem. Methods: From November 2020 to July 2021, a total of 73 patients were recruited through the University of California, Los Angeles, Center for Smart Health, after having consented to participate in this study for 2 weeks. The research concentrated on an exploratory analysis, focusing on the detailed examination of characteristics and behaviors of patients with COVID-19 as captured by the remote patient monitoring system. We collected day-to-day changes in the following sensor measurements: daily activity, daily energy expenditure, indoor localization, SpO2, respiratory rate, heart rate, and temperature. Results: Out of the 73 patients satisfying the inclusion criteria, 41 successfully adhered to using the monitoring technology, with only 22 providing substantial watch data (>4 h). Among the participants, 39 used the pulse oximeter, 37 used the thermometer, and 36 used respiratory monitoring at night. This study demonstrated an overall increase in patients' activity levels toward the end of this study, with many beginning to leave their homes after 2 weeks. Additionally, respiratory rates shifted toward healthier lower levels, and oxygen saturation improved. Fatigue and headache were identified as the most prevalent symptoms, followed by cough and loss of smell. Conclusions: The conclusion highlights the critical importance of monitoring patients outside of hospital settings, especially during pandemics, when patients travel to hospitals or receive home visits by health care professionals, which could increase the risk of disease transmission. Studies demonstrating the benefits and efficacy of remote monitoring in home settings can better prepare health care professionals for future pandemic events. Continuous monitoring of a wide range of patient metrics, from activities to vital signs, and integration of these data into electronic health records would not only improve accuracy and reduce the burden of data collection but also pave the way for enhanced home care, offering higher quality care at a lower cost.

Indexed as

COVID-19TelemedicineAdultAgedEmergenciesFemaleHumansMaleMiddle AgedMonitoring, PhysiologicRemote Patient MonitoringSARS-CoV-2Bluetooth Low Energy beaconsCOVID-19health care delivery modelsindoor localizationmobile phonephysical activityremote sensing technologysmartwatcheswearable sensors

Identifiers

PMID40680176
PMCPMC12294641

What Socratic 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 Socratic graph.