Evidence map›Paper›PMID 39709345›Full record

ArticleBMC geriatrics2024

Longitudinal changes following the introduction of socially assistive robots in nursing homes: a qualitative study with ICF framework and causal loop diagramming.

Eri Otaka, Kenji Sato, Daiki Shimotori, Tsuneki Ninomiya, Naoki Sugimoto, Yoshihiro Nakabo, Yoshio Matsumoto, Taiki Yoshida, Izumi Kondo

Abstract readMulticenter Study
In one paragraph

Article in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Eri OtakaLaboratory of Practical Technology in Community, Assistive Robot Center, National Center for Geriatrics and Gerontology Research Institute, Obu, Aichi, Japan. eotaka@ncgg.go.jp.
Kenji SatoDepartment of Rehabilitation Medicine, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan.
Daiki ShimotoriLaboratory of Practical Technology in Community, Assistive Robot Center, National Center for Geriatrics and Gerontology Research Institute, Obu, Aichi, Japan.
Tsuneki NinomiyaProduct Business Division, Fuji Soft Incorporated, Yokohama, Kanagawa, Japan.
Naoki SugimotoProduct Business Division, Fuji Soft Incorporated, Yokohama, Kanagawa, Japan.
Yoshihiro NakaboIndustrial CPS Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan.
Yoshio MatsumotoDepartment of Medical and Robotic Engineering Design, Faculty of Advanced Engineering, Tokyo University of Science, Katsushika, Tokyo, Japan.
Taiki YoshidaFaculty of Rehabilitation, Fujita Health University School of Health Sciences, Toyoake, Aichi, Japan.
Izumi KondoAssistive Robot Center, National Center for Geriatrics and Gerontology Research Institute, Obu, Aichi, Japan.

Funding

Important Projects in the Sagami Robotics Industry Special Zone, Kanagawa Prefecture Communication Robot System for the Elderly
6 · The paper itself

Abstract

backgroundSocially assistive robots introduced in nursing care settings have multidimensional psychological impacts on care recipients and caregivers. This study aims to explore the longitudinal changes induced by socially assistive robots, focusing on a chain of human behaviors.

methodsIn this qualitative study, nine participants from two nursing homes who had experience in manipulating socially assistive robots were interviewed in a semi-structured focus group using a topic guide to explore the changes in care recipients and caregivers. Following the framework analysis method, the transcripts were coded using the International Classification of Functioning, Disability, and Health (ICF). The identified codes were charted for each side-care recipients and caregivers sides- using a causal loop diagram, a tool used to visualize nonlinear dynamics in complex systems.

resultsThree and seven kinds of codes in the domains of "body functions" and "activities and participation", respectively, were identified on the care recipients' side; whereas on the caregivers' side, one and five kinds of codes in the domains of "body functions" and "activities and participation", respectively, were found. The codes obtained from the facility with longer experience were represented graphically as a reinforcing feedback loop, in which favorable changes were amplified in a chain of events. Robot use directly changed the mental functions of care recipients, and the caregivers' perceptions of these positive changes led to their own emotional and behavioral changes, which would reduce the burden of care. Moreover, the findings suggest that sharing information regarding these changes and objectively recognizing the effectiveness of robots among staff members can be the key to continuous robot use in nursing care settings. Conversely, the figure obtained from the novice facility shows fragmented chain relationships of the codes, indicating that all the effects of robot use are recognizable and form a chain reaction after continuous robot use for more than several months.

conclusionsThis study revealed important aspects regarding the longitudinal effects of socially assistive robots in nursing care settings. These implications should be broadly implemented for effective robot use and reduction of the burden of care in nursing care settings. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Nursing HomesQualitative ResearchRoboticsAgedAged, 80 and overCaregiversFemaleHomes for the AgedHumansLongitudinal StudiesMaleMiddle AgedSelf-Help DevicesAssistive roboticsBurden of careCaregiversCare recipientsICFLong-term careMental functionsStaff communication

Identifiers

PMID39709345
PMCPMC11662548

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.