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Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies
Artificial intelligence (AI)-enhanced interventions show promise for improving the delivery of long-term care (LTC) services for older people. However, the research field is developmental and has yet to be systematically synthesised. This systematic review aimed to synthesise the literature on the a...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8979827/ https://www.ncbi.nlm.nih.gov/pubmed/35515814 http://dx.doi.org/10.1016/S2666-7568(22)00034-4 |
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author | Loveys, Kate Prina, Matthew Axford, Chloe Domènec, Òscar Ristol Weng, William Broadbent, Elizabeth Pujari, Sameer Jang, Hyobum Han, Zee A Thiyagarajan, Jotheeswaran Amuthavalli |
author_facet | Loveys, Kate Prina, Matthew Axford, Chloe Domènec, Òscar Ristol Weng, William Broadbent, Elizabeth Pujari, Sameer Jang, Hyobum Han, Zee A Thiyagarajan, Jotheeswaran Amuthavalli |
author_sort | Loveys, Kate |
collection | PubMed |
description | Artificial intelligence (AI)-enhanced interventions show promise for improving the delivery of long-term care (LTC) services for older people. However, the research field is developmental and has yet to be systematically synthesised. This systematic review aimed to synthesise the literature on the acceptability and effectiveness of AI-enhanced interventions for older people receiving LTC services. We conducted a systematic search that identified 2720 records from Embase, Ovid, Global Health, PsycINFO, and Web of Science. 31 articles were included in the review that evaluated AI-enhanced social robots (n=22), environmental sensors (n=6), and wearable sensors (n=5) with older people receiving LTC services across 15 controlled and 14 non-controlled trials in high-income countries. Risk of bias was evaluated using the RoB 2, RoB 2 CRT, and ROBINS-I tools. Overall, AI-enhanced interventions were found to be somewhat acceptable to users with mixed evidence for their effectiveness across different health outcomes. The included studies were found to have high risk of bias which reduced confidence in the results. AI-enhanced interventions are promising innovations that could reshape the landscape of LTC globally. However, more trials are required to support their widespread implementation. Pathways are needed to support more high-quality trials, including in low-income and middle-income countries. |
format | Online Article Text |
id | pubmed-8979827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-89798272022-05-03 Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies Loveys, Kate Prina, Matthew Axford, Chloe Domènec, Òscar Ristol Weng, William Broadbent, Elizabeth Pujari, Sameer Jang, Hyobum Han, Zee A Thiyagarajan, Jotheeswaran Amuthavalli Lancet Healthy Longev Review Artificial intelligence (AI)-enhanced interventions show promise for improving the delivery of long-term care (LTC) services for older people. However, the research field is developmental and has yet to be systematically synthesised. This systematic review aimed to synthesise the literature on the acceptability and effectiveness of AI-enhanced interventions for older people receiving LTC services. We conducted a systematic search that identified 2720 records from Embase, Ovid, Global Health, PsycINFO, and Web of Science. 31 articles were included in the review that evaluated AI-enhanced social robots (n=22), environmental sensors (n=6), and wearable sensors (n=5) with older people receiving LTC services across 15 controlled and 14 non-controlled trials in high-income countries. Risk of bias was evaluated using the RoB 2, RoB 2 CRT, and ROBINS-I tools. Overall, AI-enhanced interventions were found to be somewhat acceptable to users with mixed evidence for their effectiveness across different health outcomes. The included studies were found to have high risk of bias which reduced confidence in the results. AI-enhanced interventions are promising innovations that could reshape the landscape of LTC globally. However, more trials are required to support their widespread implementation. Pathways are needed to support more high-quality trials, including in low-income and middle-income countries. Elsevier Ltd 2022-04 /pmc/articles/PMC8979827/ /pubmed/35515814 http://dx.doi.org/10.1016/S2666-7568(22)00034-4 Text en © 2022 World Health Organization https://creativecommons.org/licenses/by/3.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Review Loveys, Kate Prina, Matthew Axford, Chloe Domènec, Òscar Ristol Weng, William Broadbent, Elizabeth Pujari, Sameer Jang, Hyobum Han, Zee A Thiyagarajan, Jotheeswaran Amuthavalli Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
title | Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
title_full | Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
title_fullStr | Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
title_full_unstemmed | Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
title_short | Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
title_sort | artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8979827/ https://www.ncbi.nlm.nih.gov/pubmed/35515814 http://dx.doi.org/10.1016/S2666-7568(22)00034-4 |
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