Wired for Companionship: A Meta-Analysis on Social Robots Filling Void of Loneliness in Later Life

• In Event: 30860 - Section on Aging and the Life Course Roundtables
  In Refereed Roundtable (90 minute): Table 4. Social Networks, Isolation, and Aging

Abstract

Background: Loneliness is a significant public health concern affecting over a quarter of older adults worldwide, with implications for physical and mental well-being. 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 synthesizes findings from 19 studies (N= 1083) to evaluate the impact of AI-enabled social robots on loneliness among older adults. Methods: A systematic search of Scopus, EBSCO, and Web of Science platforms was conducted through October 2024. Effect sizes were synthesized using robust variance estimation (RVE) meta-analysis. Moderation analyses examined how social robots’ effectiveness differs by contextual factors such as social factors and studies’ characteristics. Publication bias was assessed using Egger’s test and funnel plots. Results: Social robots significantly reduce loneliness among older adults, though their effectiveness varies by setting. Greater reductions are observed among individuals in nursing homes and hospital settings compared to those living independently. Country-specific differences also emerge in our findings, with stronger intervention effects reported in Japan and Turkey than in the United States. However, robot type and cognitive status do not significantly influence intervention effectiveness. Implications: By highlighting the social and contextual factors that shape the effectiveness of AI-enabled social robots, this study contributes to sociological discussions on aging, technology, and social support, emphasizing the need for culturally and institutionally responsive interventions. Future research should consider cultural attitudes, long-term engagement, and personalization to optimize outcomes.

Authors

• Fahimeh Mehrabi, University of Calgary

• Akram Ghezelbash, Haskayne School of Business, University of Calgary, Calgary, AB, Canada