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Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study
BACKGROUND: Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. OBJECTIVE: The...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516685/ https://www.ncbi.nlm.nih.gov/pubmed/32723711 http://dx.doi.org/10.2196/19554 |
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author | Wilmink, Gerald Dupey, Katherine Alkire, Schon Grote, Jeffrey Zobel, Gregory Fillit, Howard M Movva, Satish |
author_facet | Wilmink, Gerald Dupey, Katherine Alkire, Schon Grote, Jeffrey Zobel, Gregory Fillit, Howard M Movva, Satish |
author_sort | Wilmink, Gerald |
collection | PubMed |
description | BACKGROUND: Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. OBJECTIVE: The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. METHODS: Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. RESULTS: The residents of the +CP and –CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. CONCLUSIONS: The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial. |
format | Online Article Text |
id | pubmed-7516685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-75166852020-10-09 Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study Wilmink, Gerald Dupey, Katherine Alkire, Schon Grote, Jeffrey Zobel, Gregory Fillit, Howard M Movva, Satish JMIR Aging Original Paper BACKGROUND: Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. OBJECTIVE: The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. METHODS: Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. RESULTS: The residents of the +CP and –CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. CONCLUSIONS: The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial. JMIR Publications 2020-09-10 /pmc/articles/PMC7516685/ /pubmed/32723711 http://dx.doi.org/10.2196/19554 Text en ©Gerald Wilmink, Katherine Dupey, Schon Alkire, Jeffrey Grote, Gregory Zobel, Howard M Fillit, Satish Movva. Originally published in JMIR Aging (http://aging.jmir.org), 10.09.2020. 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 Aging, is properly cited. The complete bibliographic information, a link to the original publication on http://aging.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Wilmink, Gerald Dupey, Katherine Alkire, Schon Grote, Jeffrey Zobel, Gregory Fillit, Howard M Movva, Satish Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study |
title | Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study |
title_full | Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study |
title_fullStr | Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study |
title_full_unstemmed | Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study |
title_short | Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study |
title_sort | artificial intelligence–powered digital health platform and wearable devices improve outcomes for older adults in assisted living communities: pilot intervention study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516685/ https://www.ncbi.nlm.nih.gov/pubmed/32723711 http://dx.doi.org/10.2196/19554 |
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