Kabacińska, K., Prescott, T.J. orcid.org/0000-0003-4927-5390 and Robillard, J.M. (2021) Socially assistive robots as mental health interventions for children : a scoping review. International Journal of Social Robotics, 13 (5). pp. 919-935. ISSN 1875-4791
Abstract
Socially Assistive Robots are promising in their potential to promote and support mental health in children. There is a growing number of studies investigating the feasibility and effectiveness of robot interventions in supporting children’s mental wellbeing. Although preliminary evidence suggests that Socially Assistive Robots may have the potential to help address concerns such as stress and anxiety in children, there is a need for a greater focus in examining the impact of robotic interventions in this population. In order to better understand the current state of the evidence in this field and identify critical gaps, we carried out a scoping review of the available literature examining how social robots are investigated as means to support mental health in children. We identified existing types of robot intervention and measures that are being used to investigate specific mental health outcomes. Overall, our findings suggest that robot interventions for children may positively impact mental health outcomes such as relief of distress and increase positive affect. Results also show that the strength of evidence needs to be improved to determine what types of robotic interventions could be most effective and readily implemented in pediatric mental health care. Based on our findings, we propose a set of recommendations to guide further research in this area.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Springer Nature B.V. This is an author-produced version of a paper subsequently published in International Journal of Social Robotics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | social robot; mental health; anxiety; distress; pediatric |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Oct 2020 06:30 |
Last Modified: | 24 Jan 2022 12:18 |
Status: | Published |
Publisher: | Springer Nature |
Refereed: | Yes |
Identification Number: | 10.1007/s12369-020-00679-0 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166270 |