Dataset Information

Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies.

ABSTRACT: 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.

SUBMITTER: Loveys K

PROVIDER: S-EPMC8979827 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies.

Loveys Kate K Prina Matthew M Axford Chloe C Domènec Òscar Ristol ÒR Weng William W Broadbent Elizabeth E Pujari Sameer S Jang Hyobum H Han Zee A ZA Thiyagarajan Jotheeswaran Amuthavalli JA

The lancet. Healthy longevity 20220401 4

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, Psy ...[more]

PMID: 35515814

Similar Datasets

Project description:BackgroundThe global aging population poses critical challenges for long-term care (LTC), including workforce shortages, escalating health care costs, and increasing demand for high-quality care. Integrating artificial intelligence (AI), the Internet of Things (IoT), and edge intelligence (EI) offers transformative potential to enhance care quality, improve safety, and streamline operations. However, existing research lacks a comprehensive analysis that synthesizes academic trends, public interest, and deeper insights regarding these technologies.ObjectiveThis study aims to provide a holistic overview of AI, IoT, and EI applications in LTC for older adults through a comprehensive bibliometric analysis, public interest insights from Google Trends, and content analysis of the top-cited research papers.MethodsBibliometric analysis was conducted using data from Web of Science, PubMed, and Scopus to identify key themes and trends in the field, while Google Trends was used to assess public interest. A content analysis of the top 1% of most-cited papers provided deeper insights into practical applications.ResultsA total of 6378 papers published between 2014 and 2023 were analyzed. The bibliometric analysis revealed that the United States, China, and Canada are leading contributors, with strong thematic overlaps in areas such as dementia care, machine learning, and wearable health monitoring technologies. High correlations were found between academic and public interest, in key topics such as "long-term care" (τ=0.89, P<.001) and "caregiver" (τ=0.72, P=.004). The content analysis demonstrated that social robots, particularly PARO, significantly improved mood and reduced agitation in patients with dementia. However, limitations, including small sample sizes, short study durations, and a narrow focus on dementia care, were noted.ConclusionsAI, IoT, and EI collectively form a powerful ecosystem in LTC settings, addressing different aspects of care for older adults. Our study suggests that increased international collaboration and the integration of emerging themes such as "rehabilitation," "stroke," and "mHealth" are necessary to meet the evolving care needs of this population. Additionally, incorporating high-interest keywords such as "machine learning," "smart home," and "caregiver" can enhance discoverability and relevance for both academic and public audiences. Future research should focus on expanding sample sizes, conducting long-term multicenter trials, and exploring broader health conditions beyond dementia, such as frailty and depression.

Project description:BackgroundObesity is a known cardiovascular risk factor and associated with higher postoperative complication rates in patients undergoing cardiac surgery. In heart failure (HF), conflicting evidence in terms of survival has been reported, whereas sarcopenia is associated with poor prognosis. An increasing number of HF patients require left ventricular assist device (LVAD) implantations. The postoperative mortality has improved in recent years but is still relatively high. The impact of body composition on outcome in this population remains unclear. The aim of this investigation was to examine the preoperative computed tomography (CT) body composition as a predictor of the postoperative outcome in advanced HF patients, who receive LVAD implantations.MethodsPreoperative CT scans of 137 patients who received LVADs between 2015 and 2020 were retrospectively analysed using an artificial intelligence (AI)-powered automated software tool based on a convolutional neural network, U-net, developed for image segmentation (Visage Version 7.1, Visage Imaging GmbH, Berlin, Germany). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT and SAT), psoas and total abdominal muscle areas and sarcopenia (defined by lumbar skeletal muscle indexes). The body composition parameters were correlated with postoperative major complication rates, survival and postoperative 6-min walk distance (6MWD) and quality of life (QoL).ResultsThe mean age of patients was 58.21 ± 11.9 years; 122 (89.1%) were male. Most patients had severe HF requiring inotropes (Interagency Registry for Mechanically Assisted Circulatory Support [INTERMACS] profile I-III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). Forty-four (32.1%) patients were obese (body mass index ≥ 30 kg/m2 ), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of postoperative infections (VAT 172.23 cm2 [54.96, 288.32 cm2 ] vs. 124.04 cm2 [56.57, 186.25 cm2 ], P = 0.022) and in-hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2 ] vs. 135.42 cm2 [49.44, 227.91 cm2 ], P = 0.033; SAT 227.28 cm2 [139.38, 304.35 cm2 ] vs. 173.81 cm2 [97.65, 254.16 cm2 ], P = 0.009). Obese patients showed no improvement of 6MWD and QoL within 6 months postoperatively (obese: +0.94 ± 161.44 months, P = 0.982; non-obese: +166.90 ± 139.00 months, P < 0.000; obese: +0.088 ± 0.421, P = 0.376; non-obese: +0.199 ± 0.324, P = 0.002, respectively). Sarcopenia did not influence the postoperative outcome and survival within 1 year after LVAD implantation.ConclusionsPreoperative AI-based CT body composition identifies patients with poor outcome after LVAD implantation. Greater adipose tissue areas are associated with an increased risk for postoperative infections, in-hospital mortality and impaired 6MWD and QoL within 6 months postoperatively.

Project description:IntroductionOlder people receiving healthcare in long-term care contexts (eg, home healthcare, sheltered housing and nursing home contexts) are especially vulnerable to developing frailty and functional decline. Considering the negative effects associated with these conditions and the possibility of preventing them from progressing, it is vital that nurses possess a broad knowledge base related to them. Particularly as prevention related to these conditions lies well within their remit. Such knowledge could guide the development of effective models of care, ensuring continuity and, hence, quality of care. Our objective will be to review published literature on existing models of care targeting frailty and/or functional decline and how these conditions are described by older people themselves, significant others and nurses in relation to long-term care.Methods and analysisThe scoping review will be conducted in accordance with Arksey and O'Malley's methodological framework. Recent methodological developments will be considered. PubMed, CINAHL and PsycINFO will be searched. Eligibility criteria will be peer-reviewed papers and written in English. All types of study designs will be eligible and included papers will be quality and ethically assessed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-Protocol checklist for protocols and the PRISMA for Scoping Reviews checklist were followed in this paper.Ethics and disseminationAs the study outlined in this protocol is a scoping review, no ethics approval was needed for this protocol nor for the upcoming study. The findings will be published in an open-access, peer-reviewed journal. Additionally, the findings will guide a research project following the Medical Research Council's framework for developing and evaluating complex interventions. Thus, supporting us in developing a model of care related to the detection and prevention of frailty and/or functional decline among older people in a long-term care context.

Dataset Information

Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies.

Publications

Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets