Evidence map›Paper›PMID 42329684›Full record

ArticleJMIR medical informatics2026

Clinical Evaluation of a Real-Time Wearable System for Monitoring In-Hospital Ambulatory Patients With COVID-19: Retrospective Data Study.

Sarah Vollam, Cristian Roman, Mauro Santos, Marco Pimentel, Oliver Redfern, Lionel Tarassenko, Peter Watkinson

Abstract read
In one paragraph

Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sarah VollamNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-2835-6271
Cristian Roman *NIHR Oxford Biomedical Research Centre, Oxford, United Kingdom.ORCID 0000-0002-9164-8659
Mauro SantosInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-1470-6966
Marco PimentelInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-3696-8852
Oliver RedfernNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-3596-3806
Lionel TarassenkoNIHR Oxford Biomedical Research Centre, Oxford, United Kingdom.ORCID 0000-0002-0118-1646
Peter WatkinsonNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-1023-3927

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany studies have evaluated the use of wearable monitoring systems to improve patient safety in hospital. Although some have demonstrated effects on intensive care admissions, there remains little evidence of impact on patient outcomes such as mortality, hospital length of stay, and time to antibiotic administration. Very few studies have focused on how wearable monitoring systems are used in clinical practice, including how the rate of manual vital sign measurements (MVSMs) is affected.

objectiveOur primary aim was to describe the physiological pattern of vital signs in hospitalized patients treated for COVID-19 outside of critical care. We also report an exploratory post hoc analysis of the impact of displaying wearable monitoring system data on the frequency of intermittent MVSMs.

methodsWe conducted a retrospective study during the COVID-19 pandemic following deployment of a wearable monitoring system that continuously displayed heart rate, respiratory rate, and oxygen saturation levels. We included patients treated for COVID-19 in 3 isolation wards in a large UK hospital. Wearable monitoring system data were displayed on a dashboard in the center of each ward. We analyzed the patterns of vital signs in patients monitored using the wearable monitoring system. We compared the time to next observation (led by nursing staff) for routinely collected MVSMs between periods when patients were continuously monitored and those when they were not. In exploratory post hoc analysis, we tested whether the difference varied between stable (early warning score [EWS] above the escalation threshold) and unstable patients.

resultsPatients (N=144) had continuous vital signs above the EWS threshold for escalation for 32.7% (2133/6528) of time monitored. The unadjusted median time between MVSMs for continuously monitored periods was 39 minutes (95% CI 29-49; P<.001) longer than for unmonitored periods. When adjusted for EWS category and participant-level clustering, the effect was attenuated but remained significant (14.6 minutes; P<.001). In exploratory post hoc analysis, we found that increases were larger during stable observation periods (51 minutes, 95% CI 39-62; P<.001) than during unstable periods (16 minutes, 95% CI 8-24; P<.001). However, adjusted analyses did not support a significant difference between stable and unstable periods.

conclusionsPatients in this study were at elevated risk of deterioration, spending a third of monitored time at or above the escalation threshold. We found that, by offering additional vital sign data between manual measurements, the time between routine MVSMs increased, which may reflect changes in nursing task prioritization. Although patient safety outcomes were not directly measured, we found no indication that reducing observation frequency adversely affected patient safety.

Indexed as

Ambulatory CareCOVID-19Wearable Electronic DevicesAgedFemaleHeart RateHumansMaleMiddle AgedMonitoring, PhysiologicRemote Patient MonitoringRetrospective StudiesSARS-CoV-2United KingdomVital Signsambulatory monitoringearly warning scorenurse workloadpatient safetyvital signswearable devices

Identifiers

PMID42329684
PMCPMC13338678

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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.