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AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults
In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473035/ https://www.ncbi.nlm.nih.gov/pubmed/34577455 http://dx.doi.org/10.3390/s21186249 |
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author | Nakaoku, Yuriko Ogata, Soshiro Murata, Shunsuke Nishimori, Makoto Ihara, Masafumi Iihara, Koji Takegami, Misa Nishimura, Kunihiro |
author_facet | Nakaoku, Yuriko Ogata, Soshiro Murata, Shunsuke Nishimori, Makoto Ihara, Masafumi Iihara, Koji Takegami, Misa Nishimura, Kunihiro |
author_sort | Nakaoku, Yuriko |
collection | PubMed |
description | In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment. |
format | Online Article Text |
id | pubmed-8473035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84730352021-09-28 AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults Nakaoku, Yuriko Ogata, Soshiro Murata, Shunsuke Nishimori, Makoto Ihara, Masafumi Iihara, Koji Takegami, Misa Nishimura, Kunihiro Sensors (Basel) Article In-home monitoring systems have been used to detect cognitive decline in older adults by allowing continuous monitoring of routine activities. In this study, we investigated whether unobtrusive in-house power monitoring technologies could be used to predict cognitive impairment. A total of 94 older adults aged ≥65 years were enrolled in this study. Generalized linear mixed models with subject-specific random intercepts were used to evaluate differences in the usage time of home appliances between people with and without cognitive impairment. Three independent power monitoring parameters representing activity behavior were found to be associated with cognitive impairment. Representative values of mean differences between those with cognitive impairment relative to those without were −13.5 min for induction heating in the spring, −1.80 min for microwave oven in the winter, and −0.82 h for air conditioner in the winter. We developed two prediction models for cognitive impairment, one with power monitoring data and the other without, and found that the former had better predictive ability (accuracy, 0.82; sensitivity, 0.48; specificity, 0.96) compared to the latter (accuracy, 0.76; sensitivity, 0.30; specificity, 0.95). In summary, in-house power monitoring technologies can be used to detect cognitive impairment. MDPI 2021-09-17 /pmc/articles/PMC8473035/ /pubmed/34577455 http://dx.doi.org/10.3390/s21186249 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nakaoku, Yuriko Ogata, Soshiro Murata, Shunsuke Nishimori, Makoto Ihara, Masafumi Iihara, Koji Takegami, Misa Nishimura, Kunihiro AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults |
title | AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults |
title_full | AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults |
title_fullStr | AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults |
title_full_unstemmed | AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults |
title_short | AI-Assisted In-House Power Monitoring for the Detection of Cognitive Impairment in Older Adults |
title_sort | ai-assisted in-house power monitoring for the detection of cognitive impairment in older adults |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473035/ https://www.ncbi.nlm.nih.gov/pubmed/34577455 http://dx.doi.org/10.3390/s21186249 |
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