Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — scoping review protocol; no pooled effect size planned or available

• Primary Outcome: Protocol paper — no primary outcome results available. Preliminary search identified 2,289 records from MEDLINE, CINAHL, and APA PsycINFO; 1,978 records remained after duplicate removal and eligibility-based exclusions prior to screening

• Nnt Or Sensitivity: Not applicable — scoping review design does not generate NNT, sensitivity/specificity, or hazard ratio estimates

Clinical Application

Clinical application is premature at protocol stage. The completed scoping review will be required to assess feasibility of implementing non-embodied AI digital companions in clinical and aged care settings. Key feasibility considerations will include digital literacy among older adults, infrastructure requirements, staff training, privacy and data governance, and integration with existing care models Social isolation and loneliness among older Australians represent a recognised public health priority, with the Royal Commission into Aged Care Quality and Safety (2021) highlighting unmet psychosocial needs in residential and community care. The Aged Care Act 2024 reforms emphasise person-centred care and quality of life outcomes. Non-embodied AI digital companions are not currently listed on the PBS or approved by the TGA as therapeutic devices, though they may fall under emerging digital health regulatory frameworks. The RACGP does not yet have specific guidelines on AI-based digital companions, though its digital health position statements acknowledge the growing role of technology in primary care. Australian aged care providers and primary care clinicians should monitor this scoping review's findings, as they may inform future RACGP guidance, aged care quality standards, and potential My Aged Care funding considerations. The My Health Record infrastructure and National Digital Health Strategy 2023–2028 provide a policy context in which such technologies could eventually be integrated. Older adults (typically aged 65 years and over) experiencing social isolation, loneliness, or unmet psychosocial needs in community, residential aged care, or hospital settings; specifically includes people living with dementia

Abstract

BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and unmet psychosocial needs across health and care contexts. Non-embodied AI-based digital companions have attracted growing attention for their potential to support companionship, social interaction, communication, and psychosocial well-being among older adults, including people living with dementia. However, the evidence base remains underexplored. Terminology is inconsistently applied, systems are variably defined, and studies are distributed across disciplinary silos, limiting critical investigation of how these technologies are conceptualized, designed, and evaluated. OBJECTIVE: This study aims to map and critically synthesize the existing literature on non-embodied AI-based digital companions for older adults in aging, health, and care contexts. The review seeks to describe digital companions, examine methodological approaches, and identify research gaps. METHODS: This protocol follows the Joanna Briggs Institute methodology for scoping reviews and will be reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines). A comprehensive search will be conducted across multiple electronic databases, including MEDLINE, APA PsycINFO, CINAHL, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, as well as selected gray literature sources. The search strategy combined terms related to digital companions, conversational AI, aging, and care contexts, including concepts related to companionship, social interaction, communication, loneliness, and psychosocial support., Eligible studies will include empirical studies involving older adults that examine non-embodied AI-based digital companions designed primarily to support companionship, social interaction, communication, or related psychosocial support in aging, health, and care contexts. Two reviewers (YC and MG) will independently conduct study selection and data charting. Data will be synthesized using descriptive statistics and narrative analysis. RESULTS: This protocol outlines a systematic approach to identifying, selecting, and synthesizing the existing evidence on non-embodied AI-based digital companions. A preliminary PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-style search flow for the revised database searches identified 2289 records from MEDLINE, CINAHL, and APA PsycINFO. After duplicate removal and eligibility-based removals before screening, 1978 records remained available for title and abstract screening. The full scoping review will summarize study characteristics, populations, contexts, digital companion features, and methodological trends using descriptive tables and narrative synthesis. As of June 2026, the revised database searches had been completed for MEDLINE, CINAHL, and APA PsycINFO, and title and abstract screening was underway. The full scoping review results are expected to be submitted for publication after screening, data charting, and synthesis are completed. CONCLUSIONS: This scoping review is expected to clarify conceptual boundaries, map the scope of current research, and identify knowledge gaps related to non-embodied AI-based digital companions in health, aging, and care contexts. The findings will inform future research, design, and implementation of non-embodied AI-based digital companions.

References

1. 1.Hung, L., Chiu, Y.-T., & Ghodsi, M. (2026). Exploring non-embodied AI-based digital companions for older adults in aging and care contexts: Protocol for a scoping review. JMIR Research Protocols. https://doi.org/10.2196/93196