Evidence map›Paper›PMID 41025480›Full record

SynthesisThe Gerontologist2025

Wired for companionship: a meta-analysis on social robots filling the void of loneliness in later life.

Fahimeh Mehrabi, Akram Ghezelbash

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in The Gerontologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. 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

2 authors.

Fahimeh MehrabiDepartment of Sociology, University of Calgary, Calgary, Alberta, Canada.ORCID 0000-0001-9715-6543
Akram GhezelbashFaculty of Economics and Business Administration, Ferdowsi University of Mashhad, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesLoneliness is a significant public health concern affecting over a quarter of older adults worldwide. Emerging research suggests that artificial intelligence (AI)-enabled social robots may offer a viable alternative for providing a new form of social support and reducing loneliness. This meta-analysis evaluates the effectiveness of AI-enabled social robots in reducing loneliness among older adults and examines the conditions under which these interventions are most effective. RESEARCH DESIGN AND

methodsA systematic search was conducted through October 2024. Effect sizes from 19 studies (N = 1,083) were synthesized using robust variance estimation (RVE) in meta-regression. Moderation analyses examined how social robots' effectiveness differs by contextual factors such as participants' backgrounds and studies' characteristics.

resultsOur findings indicated that social robots significantly reduced loneliness among older adults. However, studies with control groups indicate a higher effect size. Moreover, greater reductions in loneliness are observed among individuals in institutional settings compared to those living independently. In addition, stronger intervention effects reported in Japan and Turkey than in the United States. However, age, cognitive status, robot type, duration of intervention, and year of publication did not significantly influence intervention effectiveness. DISCUSSION AND IMPLICATIONS: Findings underscore the potential of social robots as an effective and scalable approach for addressing loneliness among older adults, particularly within institutional care environments. Policymakers, gerontologists, and care providers should consider integrating AI-enabled social robots into existing care frameworks, emphasizing culturally sensitive and inclusive implementation strategies.

Indexed as

Artificial IntelligenceLonelinessRoboticsSocial SupportAgedAged, 80 and overHumansArtificial intelligenceHuman-robot interactionLonelinessSocial support

Identifiers

PMID41025480
PMCPMC12598933

What OpenQuestion holds

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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.