The University of Southampton
University of Southampton Institutional Repository

Characterising mobility and pressure exposure in community dwelling residents with pressure ulcers using monitoring technology and intelligent algorithm

Characterising mobility and pressure exposure in community dwelling residents with pressure ulcers using monitoring technology and intelligent algorithm
Characterising mobility and pressure exposure in community dwelling residents with pressure ulcers using monitoring technology and intelligent algorithm
Aim: individuals in the community with reduced mobility are at risk of exposure to prolonged lying and sitting postures, which may cause pressure ulcers. The present study combines continuous pressure monitoring technology and intelligent algorithms to evaluate posture, mobility, and pressure profiles in a cohort of community dwelling patients, who had acquired pressure ulcers.

Materials and methods: this study represents a secondary analysis of the data from the Quality Improvement project ‘Pressure Reduction through COntinuous Monitoring In the community SEtting (PROMISE)’. 22 patients with pressure ulcers were purposely selected from 105 recruited community residents. Data were collected using a commercial continuous pressure monitoring system over a period of 1–4 days, and analysed with an intelligent algorithm using machine learning to determine posture and mobility events. Duration and magnitude of pressure signatures of each static posture and exposure thresholds were identified based on a sigmoid relationship between pressure and time.

Results: patients revealed a wide range of ages (30–95 years), BMI (17.5–47 kg/m2) and a series of co-morbidities, which may have influenced the susceptibility to skin damage. Posture, mobility, and pressure data revealed a high degree of inter-subject variability. Largest duration of static postures ranged between 1.7 and 19.8 h, with 17/22 patients spending at least 60 % of their monitoring period in static postures which lasted >2 h. Data revealed that many patients spent prolonged periods with potentially harmful interface pressure conditions, including pressure gradients >60 mmHg/cm.

Conclusion: this study combined posture, mobility, and pressure data from a commercial pressure monitoring technology through an intelligent algorithm. The community residents who had acquired a pressure ulcer at the time of monitoring exhibited trends which exposed their skin and subdermal tissues to prolonged high pressures during static postures. These indicators need further validation through prospective clinical trials.
0965-206X
Caggiari, Silvia
58f49054-6ca6-429b-b499-49b93357e5ba
Aylward-Wotton, Nicci
81b77066-64f3-4be4-84b5-4bf533dffc75
Kent, Bridie
63085af0-b767-4065-ac08-d7424f74f422
Worsley, Peter R.
6d33aee3-ef43-468d-aef6-86d190de6756
Caggiari, Silvia
58f49054-6ca6-429b-b499-49b93357e5ba
Aylward-Wotton, Nicci
81b77066-64f3-4be4-84b5-4bf533dffc75
Kent, Bridie
63085af0-b767-4065-ac08-d7424f74f422
Worsley, Peter R.
6d33aee3-ef43-468d-aef6-86d190de6756

Caggiari, Silvia, Aylward-Wotton, Nicci, Kent, Bridie and Worsley, Peter R. (2024) Characterising mobility and pressure exposure in community dwelling residents with pressure ulcers using monitoring technology and intelligent algorithm. Journal of Tissue Viability. (doi:10.1016/j.jtv.2024.07.005).

Record type: Article

Abstract

Aim: individuals in the community with reduced mobility are at risk of exposure to prolonged lying and sitting postures, which may cause pressure ulcers. The present study combines continuous pressure monitoring technology and intelligent algorithms to evaluate posture, mobility, and pressure profiles in a cohort of community dwelling patients, who had acquired pressure ulcers.

Materials and methods: this study represents a secondary analysis of the data from the Quality Improvement project ‘Pressure Reduction through COntinuous Monitoring In the community SEtting (PROMISE)’. 22 patients with pressure ulcers were purposely selected from 105 recruited community residents. Data were collected using a commercial continuous pressure monitoring system over a period of 1–4 days, and analysed with an intelligent algorithm using machine learning to determine posture and mobility events. Duration and magnitude of pressure signatures of each static posture and exposure thresholds were identified based on a sigmoid relationship between pressure and time.

Results: patients revealed a wide range of ages (30–95 years), BMI (17.5–47 kg/m2) and a series of co-morbidities, which may have influenced the susceptibility to skin damage. Posture, mobility, and pressure data revealed a high degree of inter-subject variability. Largest duration of static postures ranged between 1.7 and 19.8 h, with 17/22 patients spending at least 60 % of their monitoring period in static postures which lasted >2 h. Data revealed that many patients spent prolonged periods with potentially harmful interface pressure conditions, including pressure gradients >60 mmHg/cm.

Conclusion: this study combined posture, mobility, and pressure data from a commercial pressure monitoring technology through an intelligent algorithm. The community residents who had acquired a pressure ulcer at the time of monitoring exhibited trends which exposed their skin and subdermal tissues to prolonged high pressures during static postures. These indicators need further validation through prospective clinical trials.

Text
1-s2.0-S0965206X24001104-main - Proof
Download (2MB)

More information

Accepted/In Press date: 12 July 2024
e-pub ahead of print date: 14 July 2024

Identifiers

Local EPrints ID: 492886
URI: http://eprints.soton.ac.uk/id/eprint/492886
ISSN: 0965-206X
PURE UUID: 00ce93ad-d148-42c7-b4e2-c8961b0f6adf
ORCID for Silvia Caggiari: ORCID iD orcid.org/0000-0002-8928-2141
ORCID for Peter R. Worsley: ORCID iD orcid.org/0000-0003-0145-5042

Catalogue record

Date deposited: 19 Aug 2024 16:46
Last modified: 20 Aug 2024 02:00

Export record

Altmetrics

Contributors

Author: Silvia Caggiari ORCID iD
Author: Nicci Aylward-Wotton
Author: Bridie Kent

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×