Implementation of pressure monitoring and a risk algorithm to evaluate pre- and post-interventions in the community

Caggiari, Silvia, Aylward-Wotton, Nicci and Worsley, Peter R. (2025) Implementation of pressure monitoring and a risk algorithm to evaluate pre- and post-interventions in the community. Journal of Wound Care, 34 (9), 687-688. (doi:10.12968/jowc.2025.0370).

Abstract

Introduction: individual's residing in the community spend a relatively small amount of time with healthcare practitioners and often rely on informal or formal carers.1 Those with mobility impairments can spend prolonged periods in bed or chair, posing them at risk of pressure ulcers (PUs).2 The quality improvement project, ‘Pressure Reduction through cOntinuous Monitoring In the community SEtting’ (PROMISE), implemented the use of continuous pressure monitoring (CPM) technology in the community to inform support surface selection, posture and pressure relieving movements.3 The present study aimed to evaluate continuous pressure monitoring data using a novel algorithm developed by the researchers, to assess for changes pre- and post-PROMISE quality improvement intervention.4

Method: patients were selected from recruited community residents, whose pressure data were captured pre- and post-PROMISE intervention. Pressure data were collected with a commercial pressure monitoring (ForesitePT, Xsensor, Canada). Data was analysed with an intelligent algorithm5 to determine duration and magnitude of peak pressure gradient and peak pressure index at the buttock area. A risk algorithm,4 based on the sigmoid relationship between pressure and time to stratify exposure to prolonged pressures, determined the ‘low’ (green), ‘moderate’ (yellow), ‘high’ (orange), and ‘very high’ (red) categories of pressures and time exposure. Sigmoid curves were developed for both pressure gradients and peak pressure index parameters.

Results: in all, 17 patients were included in the study. The percentage monitoring time in each exposure category (green, yellow, orange, red), pre- and post-PROMISE intervention was assessed, with respect to duration and magnitude of peak pressure gradient and peak pressure index. Some patients spent most of their time in the ‘at risk’ categories both pre- and post-intervention. By contrast, other patients revealed mobility and pressure signatures falling in the green category for >80% of their time. There was a trend in reduced exposure categories from pre-to post-PROMISE intervention, as depicted by a shift in categories.

Conclusion: patients had a PU at the time of monitoring pre-PROMISE intervention and many exhibited trends which exposed their skin to prolonged pressures during static postures. The algorithm depicted high exposure to prolonged pressures, which showed trends of improvement post-PROMISE intervention. Further development is required to establish subject-specific sigmoids. This could be integrated in a novel sensing array for community monitoring use, and aid targeted intervention and clinical decision-making.

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