Tracking agitation in people living with dementia in a care environment

• URL: http://arxiv.org/abs/2104.09305v1
• Date: Thu, 15 Apr 2021 06:21:29 GMT
• Title: Tracking agitation in people living with dementia in a care environment
• Authors: Shehroz S. Khan, Thaejaesh Sooriyakumaran, Katherine Rich, Sofija Spasojevic, Bing Ye, Kristine Newman, Andrea Iaboni, Alex Mihailidis
• Abstract summary: Agitation is a symptom that communicates distress in people living with dementia (PwD) Care staff track and document these symptoms as a way to detect when there has been a change in resident status to assess risk, and to monitor for response to interventions. This documentation can be time-consuming, and due to staffing constraints, episodes of agitation may go unobserved. This paper presents the outcomes of a 2 year real-world study performed in a dementia unit, where a multi-modal wearable device was worn by $20$ PwD.
• Score: 2.8935683517021773
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk. In a long term care (LTC) environment, care staff track and document these symptoms as a way to detect when there has been a change in resident status to assess risk, and to monitor for response to interventions. However, this documentation can be time-consuming, and due to staffing constraints, episodes of agitation may go unobserved. This brings into question the reliability of these assessments, and presents an opportunity for technology to help track and monitor behavioural symptoms in dementia. In this paper, we present the outcomes of a 2 year real-world study performed in a dementia unit, where a multi-modal wearable device was worn by $20$ PwD. In line with a commonly used clinical documentation tool, this large multi-modal time-series data was analyzed to track the presence of episodes of agitation in 8-hour nursing shifts. The development of a baseline classification model (AUC=0.717) on this dataset and subsequent improvement (AUC= 0.779) lays the groundwork for automating the process of annotating agitation events in nursing charts.

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