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Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial

BACKGROUND: Most frail older persons are living at home, and we face difficulties in achieving seamless monitoring to detect adverse health changes. Even more important, this lack of follow-up could have a negative impact on the living choices made by older individuals and their care partners. Peopl...

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Autores principales: Piau, Antoine, Lepage, Benoit, Bernon, Carole, Gleizes, Marie-Pierre, Nourhashemi, Fati
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6887822/
https://www.ncbi.nlm.nih.gov/pubmed/31738180
http://dx.doi.org/10.2196/14245
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author Piau, Antoine
Lepage, Benoit
Bernon, Carole
Gleizes, Marie-Pierre
Nourhashemi, Fati
author_facet Piau, Antoine
Lepage, Benoit
Bernon, Carole
Gleizes, Marie-Pierre
Nourhashemi, Fati
author_sort Piau, Antoine
collection PubMed
description BACKGROUND: Most frail older persons are living at home, and we face difficulties in achieving seamless monitoring to detect adverse health changes. Even more important, this lack of follow-up could have a negative impact on the living choices made by older individuals and their care partners. People could give up their homes for the more reassuring environment of a medicalized living facility. We have developed a low-cost unobtrusive sensor-based solution to trigger automatic alerts in case of an acute event or subtle changes over time. It could facilitate older adults’ follow-up in their own homes, and thus support independent living. OBJECTIVE: The primary objective of this prospective open-label study is to evaluate the relevance of the automatic alerts generated by our artificial intelligence–driven monitoring solution as judged by the recipients: older adults, caregivers, and professional support workers. The secondary objective is to evaluate its ability to detect subtle functional and cognitive decline and major medical events. METHODS: The primary outcome will be evaluated for each successive 2-month follow-up period to estimate the progression of our learning algorithm performance over time. In total, 25 frail or disabled participants, aged 75 years and above and living alone in their own homes, will be enrolled for a 6-month follow-up period. RESULTS: The first phase with 5 participants for a 4-month feasibility period has been completed and the expected completion date for the second phase of the study (20 participants for 6 months) is July 2020. CONCLUSIONS: The originality of our real-life project lies in the choice of the primary outcome and in our user-centered evaluation. We will evaluate the relevance of the alerts and the algorithm performance over time according to the end users. The first-line recipients of the information are the older adults and their care partners rather than health care professionals. Despite the fast pace of electronic health devices development, few studies have addressed the specific everyday needs of older adults and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT03484156; https://clinicaltrials.gov/ct2/show/NCT03484156 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/14245
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spelling pubmed-68878222019-12-12 Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial Piau, Antoine Lepage, Benoit Bernon, Carole Gleizes, Marie-Pierre Nourhashemi, Fati JMIR Res Protoc Protocol BACKGROUND: Most frail older persons are living at home, and we face difficulties in achieving seamless monitoring to detect adverse health changes. Even more important, this lack of follow-up could have a negative impact on the living choices made by older individuals and their care partners. People could give up their homes for the more reassuring environment of a medicalized living facility. We have developed a low-cost unobtrusive sensor-based solution to trigger automatic alerts in case of an acute event or subtle changes over time. It could facilitate older adults’ follow-up in their own homes, and thus support independent living. OBJECTIVE: The primary objective of this prospective open-label study is to evaluate the relevance of the automatic alerts generated by our artificial intelligence–driven monitoring solution as judged by the recipients: older adults, caregivers, and professional support workers. The secondary objective is to evaluate its ability to detect subtle functional and cognitive decline and major medical events. METHODS: The primary outcome will be evaluated for each successive 2-month follow-up period to estimate the progression of our learning algorithm performance over time. In total, 25 frail or disabled participants, aged 75 years and above and living alone in their own homes, will be enrolled for a 6-month follow-up period. RESULTS: The first phase with 5 participants for a 4-month feasibility period has been completed and the expected completion date for the second phase of the study (20 participants for 6 months) is July 2020. CONCLUSIONS: The originality of our real-life project lies in the choice of the primary outcome and in our user-centered evaluation. We will evaluate the relevance of the alerts and the algorithm performance over time according to the end users. The first-line recipients of the information are the older adults and their care partners rather than health care professionals. Despite the fast pace of electronic health devices development, few studies have addressed the specific everyday needs of older adults and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT03484156; https://clinicaltrials.gov/ct2/show/NCT03484156 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/14245 JMIR Publications 2019-11-18 /pmc/articles/PMC6887822/ /pubmed/31738180 http://dx.doi.org/10.2196/14245 Text en ©Antoine Piau, Benoit Lepage, Carole Bernon, Marie-Pierre Gleizes, Fati Nourhashemi. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 18.11.2019. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Protocol
Piau, Antoine
Lepage, Benoit
Bernon, Carole
Gleizes, Marie-Pierre
Nourhashemi, Fati
Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial
title Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial
title_full Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial
title_fullStr Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial
title_full_unstemmed Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial
title_short Real-Time Detection of Behavioral Anomalies of Older People Using Artificial Intelligence (The 3-PEGASE Study): Protocol for a Real-Life Prospective Trial
title_sort real-time detection of behavioral anomalies of older people using artificial intelligence (the 3-pegase study): protocol for a real-life prospective trial
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6887822/
https://www.ncbi.nlm.nih.gov/pubmed/31738180
http://dx.doi.org/10.2196/14245
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