Imperial College London
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ProfessorDavidSharp

Faculty of Medicine, Department of Brain Sciences

Professor of Neurology
 
 
 
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Contact

 

+44 (0)20 7594 7991david.sharp Website

 
 
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Location

 

UREN.927Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Fletcher-Lloyd:2021:10.1002/alz.058614,
author = {Fletcher-Lloyd, N and Soreq, E and Wilson, D and Nilforooshan, R and Sharp, DJ and Barnaghi, P},
doi = {10.1002/alz.058614},
journal = {Alzheimers & Dementia},
pages = {1--1},
title = {Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.},
url = {http://dx.doi.org/10.1002/alz.058614},
volume = {17},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: People living with dementia (PLWD) have an increased susceptibility to developing adverse physical and psychological events. Internet of Things (IoT) technologies provides new ways to remotely monitor patients within the comfort of their homes, particularly important for the timely delivery of appropriate healthcare. Presented here is data collated as part of the on-going UK Dementia Research Institute's Care Research and Technology Centre cohort and Technology Integrated Health Management (TIHM) study. There are two main aims to this work: first, to investigate the effect of the COVID-19 quarantine on the performance of daily living activities of PLWD, on which there is currently little research; and second, to create a simple classification model capable of effectively predicting agitation risk in PLWD, allowing for the generation of alerts with actionable information by which to prevent such outcomes. METHOD: A within-subject, date-matched study was conducted on daily living activity data using the first COVID-19 quarantine as a natural experiment. Supervised machine learning approaches were then applied to combined physiological and environmental data to create two simple classification models: a single marker model trained using ambient temperature as a feature, and a multi-marker model using ambient temperature, body temperature, movement, and entropy as features. RESULT: There are 102 PLWD total included in the dataset, with all patients having an established diagnosis of dementia, but with ranging types and severity. The COVID-19 study was carried out on a sub-group of 21 patient households. In 2020, PLWD had a significant increase in daily household activity (p = 1.40e-08), one-way repeated measures ANOVA). Moreover, there was a significant interaction between the pandemic quarantine and patient gender on night-time bed-occupancy duration (p = 3.00e-02, two-way mixed-effect ANOVA). On evaluating the models using 10-fold cross validation, both th
AU  - Fletcher-Lloyd,N
AU  - Soreq,E
AU  - Wilson,D
AU  - Nilforooshan,R
AU  - Sharp,DJ
AU  - Barnaghi,P
DO  - 10.1002/alz.058614
EP  - 1
PY  - 2021///
SN  - 1552-5260
SP  - 1
TI  - Home monitoring of daily living activities and prediction of agitation risk in a cohort of people living with dementia.
T2  - Alzheimers & Dementia
UR  - http://dx.doi.org/10.1002/alz.058614
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34971120
UR  - https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.058614
VL  - 17
ER  -
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