Evidence map›Paper›PMID 39658003›Full record

ArticleJMIR biomedical engineering2024

User Perceptions of Wearability of Knitted Sensor Garments for Long-Term Monitoring of Breathing Health: Thematic Analysis of Focus Groups and a Questionnaire Survey.

Kristel Fobelets, Nikita Mohanty, Mara Thielemans, Lieze Thielemans, Gillian Lake-Thompson, Meijing Liu, Kate Jopling, Kai Yang

Abstract read
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Article in JMIR biomedical engineering, 2024. 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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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

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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

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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

8 authors.

Kristel FobeletsDepartment of Electrical and Electronic Engineering, Imperial College London, South Kensington, United Kingdom.ORCID https://orcid.org/0000-0001-5607-8243
Nikita MohantyDepartment of Electrical and Electronic Engineering, Imperial College London, South Kensington, United Kingdom.ORCID https://orcid.org/0009-0003-7157-8336
Mara ThielemansDepartment of Electrical and Electronic Engineering, Imperial College London, South Kensington, United Kingdom.ORCID https://orcid.org/0009-0001-1121-9804
Lieze ThielemansRoyal Free London NHS Foundation Trust, London, United Kingdom.ORCID https://orcid.org/0000-0003-2391-5695
Gillian Lake-ThompsonWSA E-Textile Innovation Lab, Winchester School of Art, University of Southampton, Winchester, United Kingdom.ORCID https://orcid.org/0009-0005-1636-0013
Meijing LiuWSA E-Textile Innovation Lab, Winchester School of Art, University of Southampton, Winchester, United Kingdom.ORCID https://orcid.org/0000-0001-8334-7533
Kate JoplingWSA E-Textile Innovation Lab, Winchester School of Art, University of Southampton, Winchester, United Kingdom.ORCID https://orcid.org/0000-0001-5019-0816
Kai YangWSA E-Textile Innovation Lab, Winchester School of Art, University of Southampton, Winchester, United Kingdom.ORCID https://orcid.org/0000-0001-7497-3911

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong-term unobtrusive monitoring of breathing patterns can potentially give a more realistic insight into the respiratory health of people with asthma or chronic obstructive pulmonary disease than brief tests performed in medical environments. However, it is uncertain whether users would be willing to wear these sensor garments long term.

objectiveOur objective was to explore whether users would wear ordinary looking knitted garments with unobtrusive knitted-in breathing sensors long term to monitor their lung health and under what conditions.

methodsMultiple knitted breathing sensor garments, developed and fabricated by the research team, were presented during a demonstration. Participants were encouraged to touch and feel the garments and ask questions. This was followed by two semistructured, independently led focus groups with a total of 16 adults, of whom 4 had asthma. The focus group conversations were recorded and transcribed. Thematic analysis was carried out by three independent researchers in 3 phases consisting of familiarization with the data, independent coding, and overarching theme definition. Participants also completed a web-based questionnaire to probe opinion about wearability and functionality of the garments. Quantitative analysis of the sensors' performance was mapped to participants' garment preference to support the feasibility of the technology for long-term wear.

resultsKey points extracted from the qualitative data were (1) garments are more likely to be worn if medically prescribed, (2) a cotton vest worn as underwear was preferred, and (3) a breathing crisis warning system was seen as a promising application. The qualitative analysis showed a preference for a loose-fitting garment style with short sleeves (13/16 participants), 11 out of 16 would also wear snug fitting garments and none of the participants would wear tight-fitting garments over a long period of time. In total, 10 out of 16 participants would wear the snug fitting knitted garment for the whole day and 13 out of 16 would be happy to wear it only during the night if not too hot. The sensitivity demands on the knitted wearable sensors can be aligned with most users' garment preferences (snug fit).

conclusionsThere is an overall positive opinion about wearing a knitted sensor garment over a long period of time for monitoring respiratory health. The knit cannot be tight but a snugly fitted vest as underwear in a breathable material is acceptable for most participants. These requirements can be fulfilled with the proposed garments. Participants with asthma supported using it as a sensor garment connected to an asthma attack alert system.

Indexed as

asthma observationbreathingfocus groupfocus groupshealth monitoringhealth technologylungmedical devicemedical equipmentmedical instrumentmedical toolmonitormonitoringphysiological sensorrespiratoryrespirologysensorwearability of knitted sensorswearable device

Identifiers

PMID39658003
PMCPMC11668994

What OpenQuestion holds

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LicenceCC BY
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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.