Exploring the impact of socially assistive robots on health and wellbeing across the lifespan: An umbrella review and meta-analysis.

Children Health outcomes Nursing Older adults Socially assistive robots Wellbeing

Journal

International journal of nursing studies
ISSN: 1873-491X
Titre abrégé: Int J Nurs Stud
Pays: England
ID NLM: 0400675

Informations de publication

Date de publication:
22 Feb 2024
Historique:
received: 30 10 2023
revised: 14 02 2024
accepted: 15 02 2024
medline: 3 3 2024
pubmed: 3 3 2024
entrez: 2 3 2024
Statut: aheadofprint

Résumé

Socially assistive robots offer an alternate source of connection for interventions within health and social care amidst a landscape of technological advancement and reduced staff capacity. There is a need to summarise the available systematic reviews on the health and wellbeing impacts to evaluate effectiveness, explore potential moderators and mediators, and identify recommendations for future research and practice. To explore the effect of socially assistive robots within health and social care on psychosocial, behavioural, and physiological health and wellbeing outcomes across the lifespan (PROSPERO registration number: CRD42023423862). An umbrella review utilising meta-analysis, narrative synthesis, and vote counting by direction of effect. 14 databases were searched (ProQuest Health Research Premium collection, Scopus, PubMed, Web of Science, ASM Digital Library, IEEE Xplore, Cochrane Reviews, and EPISTEMONIKOS) from 2005 to May 4, 2023. Systematic reviews including the effects of socially assistive robots on health outcomes were included and a pooled meta-analysis, vote counting by direction of effect, and narrative synthesis were applied. The second version of A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) was applied to assess quality of included reviews. 35 reviews were identified, most focusing on older adults with or without dementia (n = 24). Pooled meta-analysis indicated no effect of socially assistive robots on quality of life (standard mean difference (SMD) = 0.43), anxiety (SMD = -0.02), or depression (SMD = 0.21), although vote counting identified significant improvements in social interaction, mood, positive affect, loneliness, stress, and pain across the lifespan, and narrative synthesis identified an improvement in anxiety in children. However, some reviews reported no significant difference between the effects of socially assistive robots and a plush toy, and there was no effect of socially assistive robots on psychiatric outcomes including agitation, neuropsychiatric symptoms, and medication use. Socially assistive robots show promise for improving non-psychiatric outcomes such as loneliness, positive affect, stress, and pain, but exert no effect on psychiatric outcomes such as depression and agitation. The main mechanism of effect within group settings appeared to be the stimulation of social interaction with other humans. Limitations include the low quality and high amount of overlap between included reviews. Socially assistive robots may help to improve loneliness, social interaction, and positive affect in older adults, decrease anxiety and distress in children, and improve mood, stress, and reduce pain across the lifespan. However, before recommendations for socially assistive robots can be made, a cost-effectiveness analysis of socially assistive robots to improve mood across the lifespan, and a quantitative analysis of the effects on pain, anxiety, and distress in children are required.

Sections du résumé

BACKGROUND BACKGROUND
Socially assistive robots offer an alternate source of connection for interventions within health and social care amidst a landscape of technological advancement and reduced staff capacity. There is a need to summarise the available systematic reviews on the health and wellbeing impacts to evaluate effectiveness, explore potential moderators and mediators, and identify recommendations for future research and practice.
OBJECTIVE OBJECTIVE
To explore the effect of socially assistive robots within health and social care on psychosocial, behavioural, and physiological health and wellbeing outcomes across the lifespan (PROSPERO registration number: CRD42023423862).
DESIGN METHODS
An umbrella review utilising meta-analysis, narrative synthesis, and vote counting by direction of effect.
METHODS METHODS
14 databases were searched (ProQuest Health Research Premium collection, Scopus, PubMed, Web of Science, ASM Digital Library, IEEE Xplore, Cochrane Reviews, and EPISTEMONIKOS) from 2005 to May 4, 2023. Systematic reviews including the effects of socially assistive robots on health outcomes were included and a pooled meta-analysis, vote counting by direction of effect, and narrative synthesis were applied. The second version of A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) was applied to assess quality of included reviews.
RESULTS RESULTS
35 reviews were identified, most focusing on older adults with or without dementia (n = 24). Pooled meta-analysis indicated no effect of socially assistive robots on quality of life (standard mean difference (SMD) = 0.43), anxiety (SMD = -0.02), or depression (SMD = 0.21), although vote counting identified significant improvements in social interaction, mood, positive affect, loneliness, stress, and pain across the lifespan, and narrative synthesis identified an improvement in anxiety in children. However, some reviews reported no significant difference between the effects of socially assistive robots and a plush toy, and there was no effect of socially assistive robots on psychiatric outcomes including agitation, neuropsychiatric symptoms, and medication use.
DISCUSSION CONCLUSIONS
Socially assistive robots show promise for improving non-psychiatric outcomes such as loneliness, positive affect, stress, and pain, but exert no effect on psychiatric outcomes such as depression and agitation. The main mechanism of effect within group settings appeared to be the stimulation of social interaction with other humans. Limitations include the low quality and high amount of overlap between included reviews.
CONCLUSION CONCLUSIONS
Socially assistive robots may help to improve loneliness, social interaction, and positive affect in older adults, decrease anxiety and distress in children, and improve mood, stress, and reduce pain across the lifespan. However, before recommendations for socially assistive robots can be made, a cost-effectiveness analysis of socially assistive robots to improve mood across the lifespan, and a quantitative analysis of the effects on pain, anxiety, and distress in children are required.

Identifiants

pubmed: 38430662
pii: S0020-7489(24)00042-7
doi: 10.1016/j.ijnurstu.2024.104730
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104730

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Bethany Nichol (B)

Department of Social Work, Education and Community Wellbeing, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom. Electronic address: bethany.nichol@northumbria.ac.uk.

Jemma McCready (J)

Department of Social Work, Education and Community Wellbeing, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom. Electronic address: j.mccready@northumbria.ac.uk.

Goran Erfani (G)

Department of Nursing, Midwifery and Health, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom. Electronic address: goran.erfani@northumbria.ac.uk.

Dania Comparcini (D)

Section of Nursing, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari "Aldo Moro", Bari, Italy. Electronic address: dania.comparcini@uniba.it.

Valentina Simonetti (V)

University "LUM" Giuseppe Degennaro, Casamassima, Bari, Italy. Electronic address: simonetti@lum.it.

Giancarlo Cicolini (G)

Section of Nursing, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari "Aldo Moro", Bari, Italy. Electronic address: giancarlo.cicolini@uniba.it.

Kristina Mikkonen (K)

Research Unit of Health Science and Technology, University of Oulu, Oulu, Finland; Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland. Electronic address: kristina.mikkonen@oulu.fi.

Miyae Yamakawa (M)

Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita City, Japan. Electronic address: miyatabu@sahs.med.osaka-u.ac.jp.

Marco Tomietto (M)

Department of Nursing, Midwifery and Health, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom; Research Unit of Health Science and Technology, University of Oulu, Oulu, Finland. Electronic address: marco.tomietto@northumbria.ac.uk.

Classifications MeSH