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Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study

The most frequent form of dementia is Alzheimer’s Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive d...

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Autores principales: Bellini, Gloria, Cipriano, Marco, Comai, Sara, De Angeli, Nicola, Gargano, Jacopo Pio, Gianella, Matteo, Goi, Gianluca, Ingrao, Giovanni, Masciadri, Andrea, Rossi, Gabriele, Salice, Fabio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003276/
https://www.ncbi.nlm.nih.gov/pubmed/33803913
http://dx.doi.org/10.3390/s21062147
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author Bellini, Gloria
Cipriano, Marco
Comai, Sara
De Angeli, Nicola
Gargano, Jacopo Pio
Gianella, Matteo
Goi, Gianluca
Ingrao, Giovanni
Masciadri, Andrea
Rossi, Gabriele
Salice, Fabio
author_facet Bellini, Gloria
Cipriano, Marco
Comai, Sara
De Angeli, Nicola
Gargano, Jacopo Pio
Gianella, Matteo
Goi, Gianluca
Ingrao, Giovanni
Masciadri, Andrea
Rossi, Gabriele
Salice, Fabio
author_sort Bellini, Gloria
collection PubMed
description The most frequent form of dementia is Alzheimer’s Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer’s disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place.
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spelling pubmed-80032762021-03-28 Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study Bellini, Gloria Cipriano, Marco Comai, Sara De Angeli, Nicola Gargano, Jacopo Pio Gianella, Matteo Goi, Gianluca Ingrao, Giovanni Masciadri, Andrea Rossi, Gabriele Salice, Fabio Sensors (Basel) Article The most frequent form of dementia is Alzheimer’s Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer’s disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place. MDPI 2021-03-18 /pmc/articles/PMC8003276/ /pubmed/33803913 http://dx.doi.org/10.3390/s21062147 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bellini, Gloria
Cipriano, Marco
Comai, Sara
De Angeli, Nicola
Gargano, Jacopo Pio
Gianella, Matteo
Goi, Gianluca
Ingrao, Giovanni
Masciadri, Andrea
Rossi, Gabriele
Salice, Fabio
Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study
title Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study
title_full Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study
title_fullStr Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study
title_full_unstemmed Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study
title_short Understanding Social Behaviour in a Health-Care Facility from Localization Data: A Case Study
title_sort understanding social behaviour in a health-care facility from localization data: a case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003276/
https://www.ncbi.nlm.nih.gov/pubmed/33803913
http://dx.doi.org/10.3390/s21062147
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