Evidence map›Paper›PMID 37420941›Full record

ArticleMicromachines2023

Multi-Modal Portable Respiratory Rate Monitoring Device for Childhood Pneumonia Detection.

Sadeque Reza Khan, Xiaohan Wang, Tiantao Jiang, Wei Ju, Norbert Radacsi, Muhammad Abdul Kadir, Khondkar Siddique-E Rabbani, Steve Cunningham, Srinjoy Mitra

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2023. 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
0.4field-weighted citation impact, top 44% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

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

9 authors at 4 institutions in 2 countries.

Sadeque Reza KhanSchool of Engineering and Physical Sciences, Institute of Sensors, Signals and Systems, Heriot-Watt University, Edinburgh EH14 4AS, UK.ORCID 0000-0003-1805-7999
Xiaohan WangSchool of Engineering, Institute of Integrated Micro and Nano Systems, The University of Edinburgh, Edinburgh EH9 3FF, UK.
Tiantao JiangSchool of Engineering, Institute of Integrated Micro and Nano Systems, The University of Edinburgh, Edinburgh EH9 3FF, UK.
Wei JuSchool of Engineering, Institute of Integrated Micro and Nano Systems, The University of Edinburgh, Edinburgh EH9 3FF, UK.ORCID 0000-0002-8683-9530
Norbert RadacsiSchool of Engineering, Institute for Materials and Processes, The University of Edinburgh, Robert Stevenson Road, Edinburgh EH9 3FB, UK.ORCID 0000-0002-7358-951X
Muhammad Abdul KadirDepartment of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.ORCID 0000-0002-4535-9215
Khondkar Siddique-E RabbaniDepartment of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.
Steve CunninghamCentre for Inflammation Research, The University of Edinburgh, Edinburgh EH9 3FF, UK.ORCID 0000-0001-7342-251X
Srinjoy MitraSchool of Engineering, Institute of Integrated Micro and Nano Systems, The University of Edinburgh, Edinburgh EH9 3FF, UK.
University of Edinburgh · GBUniversity of Dhaka · BDCentre for Inflammation Research · GBHeriot-Watt University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate assessment of Respiratory Rate (RR) is the most important mechanism in detecting pneumonia in low-resource settings. Pneumonia is a disease with one of the highest mortality rates among young children under five. However, the diagnosis of pneumonia for infants remains challenging, especially in low- and middle-income countries (LMIC). In such situations, RR is most often measured manually with visual inspection. Accurate RR measurement requires the child to remain calm without any stress for a few minutes. The difficulty in achieving this with a sick child in a clinical environment can result in errors and misdiagnosis, even more so when the child is crying and non-cooperating around unfamiliar adults. Therefore, we propose an automated novel RR monitoring device built with textile glove and dry electrodes which can make use of the relaxed posture when the child is resting on the carer's lap. This portable system is non-invasive and made with affordable instrumentation integrated on customized textile glove. The glove has multi-modal automated RR detection mechanism that simultaneously uses bio-impedance and accelerometer data. This novel textile glove with dry electrodes can easily be worn by a parent/carer and is washable. The real-time display on a mobile app shows the raw data and the RR value, allowing a healthcare professional to monitor the results from afar. The prototype device has been tested on 10 volunteers with age variation of 3 years to 33 years, including male and female. The maximum variation of measured RR with the proposed system is ±2 compared to the traditional manual counting method. It does not create any discomfort for either the child or the carer and can be used up to 60 to 70 sessions/day before recharging.

Indexed as

accelerometerbioimpedancebreath per minutelow- and middle-income countriesrespiratory ratetextile electrode

Identifiers

PMID37420941
PMCPMC10144858
OpenAlexW4360615543

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.