Evidence map›Paper›PMID 39935956›Full record

ArticleJournal of rehabilitation and assistive technologies engineering

"It's always happy to see me": Exploring LOVOT robots as companions for older adults.

Lillian Hung, Joey Wong, Karen Lok Yi Wong, Kelvin Cheng-Kian Tan, Vivian Wei-Qun Lou

Abstract read
In one paragraph

Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

5 authors.

Lillian HungIDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada.
Joey WongIDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada.
Karen Lok Yi WongIDEA Lab, School of Nursing, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-4079-9123
Kelvin Cheng-Kian TanS R Nathan School of Human Development, Singapore University of Social Services, Singapore.
Vivian Wei-Qun LouDepartment of Social Work and Social Administration, The University of Hong Kong, Hong Kong.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: AI-enabled social robots present the potential to resolve the loneliness and social isolation of older adults in long-term care (LTC). There is limited research on how older adults perceive and make sense of these robots and how human-robot companionship is formed. This study investigated older adults' experiences using LOVOT, a social robot. Methods: Using an ethnographic study design, we introduced LOVOT robots to a Canadian LTC home for four weekly interaction sessions. Thirty-six residents, seven family members and two healthcare staff participated. Data collection involved observational field notes and conversational interviews. The analysis was guided by ikigai, a Japanese well-being concept. Findings: Reflexive thematic analysis identified four key themes. 1) Joy: The robot offers joy and excitement through interactions. 2) Acceptance: For older adults with mobility or cognitive impairments, LOVOT gives consistent positive responses, offering a sense of unconditional acceptance. 3) Creativity: The robot's non-verbal communication allows older adults to grow creative imagination, encouraging personal expression and expanding interaction possibilities. 4) "Not for me": Not all participants like the LOVOT robot. Conclusion: AI-enabled social robots show potential in supporting the psychosocial needs of older adults, which have broader implications for LTC practices and future research directions. Future research should further explore the creative utility of social robots among LTC residents.

Indexed as

Artificial intelligencelong-term careolder adultsrobotssocial companion

Identifiers

PMID39935956
PMCPMC11811964

What OpenQuestion holds

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LicenceCC BY-NC
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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.