Abstract
Agitation is one of the most common behavioural and psychological symptoms in people living with dementia (PLwD). This behaviour can cause tremendous stress and anxiety on family caregivers and healthcare providers. Direct observation of PLwD is the traditional way to measure episodes of agitation. However, this method is subjective, bias-prone and timeconsuming. Importantly, it does not predict the onset of the agitation. Therefore, there is a need to develop a continuous monitoring system that can detect and/or predict the onset of agitation. In this study, a multi-modal sensor platform with video cameras, motion and door sensors, wristbands and pressure mats were set up in a hospital-based dementia behavioural care unit to develop a predictive system to identify the onset of agitation. The research team faced several barriers in the development and initiation of the study, namely addressing concerns about the study ethics, logistics and costs of study activities, device design for PLwD and limitations of its use in the hospital. In this paper, the strategies and methodologies that were implemented to address these challenges are discussed for consideration by future researchers who will conduct similar studies in a hospital setting.
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Notes
Actigraphy is a non-invasive method that monitors a person’s activity/rest patterns over an extended period of time using a wearable device called Actigraph (Stone and Ancoli-Israel 2011).
SDM refers to a patient’s legally authorized representative (LAR) who has the Power of Attorney (POA) for a patient’s treatment or personal care. The term SDM is used in this paper in order to keep consistent with the REB documentation.
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Acknowledgements
We would like to thank Alzheimer’s Society Research Program Grant (2016) – QoL (Grant No. 17-24) and Daphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University for their financial support to conduct this research.
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Ye, B., Khan, S.S., Chikhaoui, B. et al. Challenges in Collecting Big Data in A Clinical Environment with Vulnerable Population: Lessons Learned from A Study Using A Multi-modal Sensors Platform. Sci Eng Ethics 25, 1447–1466 (2019). https://doi.org/10.1007/s11948-018-0072-y
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DOI: https://doi.org/10.1007/s11948-018-0072-y