Evidence map›Paper›PMID 41195378›Full record

ArticleDigital health

Validation of wearable vital signs monitoring: A comparison with conventional bedside patient monitors.

Weiyi Jiang, Wenzhao Zhang, Haoxuan Li, Jean Ngoie, Wenda Li, Zhihong Huang

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

6 authors.

Weiyi JiangSchool of Physics, Engineering and Technology, University of York, York, UK.ORCID https://orcid.org/0009-0001-8244-4523
Wenzhao ZhangInstitute of Signals, Sensors and Systems, Heriot-Watt University, Edinburgh, UK.
Haoxuan LiInstitute of Signals, Sensors and Systems, Heriot-Watt University, Edinburgh, UK.ORCID https://orcid.org/0009-0007-4618-2590
Jean NgoieCHEO Research Institute, Ottawa, Ontario, Canada.
Wenda LiInstitute of Signals, Sensors and Systems, Heriot-Watt University, Edinburgh, UK.ORCID https://orcid.org/0000-0001-6617-9136
Zhihong HuangSchool of Physics, Engineering and Technology, University of York, York, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to assess how accurately mobile wearable devices could be used to monitor vital signs continuously in clinical settings by comparing their measurements with those from traditional bedside monitors. Methods: Data collected from Mindray's mWear wearable device were compared against measurements from the BeneVision N15 traditional bedside monitoring system. A total of 208 paired datasets, including blood pressure, oxygen saturation, heart rate, and respiratory rate, were collected from 16 healthy volunteers in a clinical setting. Bland-Altman analysis was applied to assess agreement between the two devices. Results: The analysis showed that 94.2% of the data variance points for oxygen saturation, diastolic blood pressure, and pulse rate fell within the limits of agreement. For systolic blood pressure, 92.3% of the data variance points were within limits, while 94.7% of the heart rate and respiratory rate data points were also within agreement limits. Conclusion: There was a strong agreement between the wearable mWear device and the traditional bedside patient monitoring system. This study supports the accuracy and reliability of wearable devices for continuous vital signs monitoring. These results encourage the wider use and ongoing improvement of wearable technology in clinical practice.

Indexed as

Bland-Altman analysisdigital healthnewborn healthpatient monitoringVital signswearableswireless

Identifiers

PMID41195378
PMCPMC12583877

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Registered trials

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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.