Challenges in collecting big data in a clinical environment with vulnerable population : Lessons learned from a study using a multi-modal sensors platform [r-libre/1520]

Ye, Bing; Khan, Shehroz S.; Chikhaoui, Belkacem; Iaboni, Andrea; Schindel Martin, Lori; Newman, Kristine; Wang, Angel et Mihailidis, Alex (2019). Challenges in collecting big data in a clinical environment with vulnerable population : Lessons learned from a study using a multi-modal sensors platform. Journal of Science and Engineering Ethics, 25 (5), 1447–1466. https://doi.org/10.1007/s11948-018-0072-y

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	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 time-consuming. 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 our study, we have set up a multi-modal sensor platform with video cameras, motion and door sensors, wristbands and pressure mats in a hospital-based dementia behavioural care unit to develop a predictive system to identify the onset of agitation. We have 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, we discussed the strategies and methodologies we implemented to address these challenges for consideration by future researchers who will conduct similar studies in a hospital setting.
Déposant:	Chikhaoui, Belkacem
Responsable :	Belkacem Chikhaoui
Dépôt :	04 oct. 2018 19:11
Dernière modification :	13 janv. 2020 16:29

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