Acceptance of AI-based assistive technologies for autonomy support among older adults: a scoping review
Abstract
Background: Population aging is leading to increased demand for health and long-term care. Artificial intelligence (AI)-based assistive technologies have been developed to support autonomy and care for older adults, yet real‑world adoption remains limited. We review evidence on acceptance and determinants of acceptance of AI‑based assistive technologies among older adults.
Methods: We conducted a scoping review using the Joanna Briggs Institute methodology and followed Preferred Reporting Items for Systematic reviews and Meta-Analyses‑Scoping Review guidelines for reporting. Four databases (PubMed, Embase, Web of Science, Cochrane) were searched for studies published from January 2010 to June 2025, initially in any language. We included studies examining acceptance and determinants of acceptance of AI-based assistive technologies among adults aged 65 years and older. We synthesized data using inductive thematic analysis.
Results: Twenty‑nine studies met the inclusion criteria, with over one-third conducted in Europe (n = 10). Overall attitudes toward AI‑based assistive technologies were generally favorable when technologies were perceived as useful, easy to use, and tailored to needs. However, actual use was low. Acceptance was associated with socio‑demographic (eg, age, residence), medical (eg, frailty, self‑rated health), psychosocial (eg, stigma), and structural (eg, cost, accessibility) determinants, as well as with institutional and relational factors (eg, preservation of human contact).
Conclusions: While older adults have generally positive views of AI‑based assistive technologies, adoption is slow because of intersecting psychosocial, economic, technical, and organizational barriers. User‑centered design, care-led innovation approaches, and implementation strategies that complement—rather than replace—human support may facilitate uptake and sustained use. Development of standardized assessment tools is needed to enable cross-study comparability and guide future research.
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BibTeX
@article{thera2026acceptance,
title = {{Acceptance of AI-based assistive technologies for autonomy support among older adults: a scoping review}},
author = {Sekou Oumarou Thera and Ali Ben Charif and Jeanne Bertona and Patrick Villani and Sébastien Lechevalier and Marc-Karim Bendiane and Anne-Laure Couderc},
journal = {The Journals of Gerontology Series A: Biological Sciences and Medical Sciences},
year = {2026},
month = sep,
doi = {10.1093/gerona/glag238},
url = {https://doi.org/10.1093/gerona/glag238},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Acceptance of AI-based assistive technologies for autonomy support among older adults: a scoping review
AU - Sekou Oumarou Thera
AU - Ali Ben Charif
AU - Jeanne Bertona
AU - Patrick Villani
AU - Sébastien Lechevalier
AU - Marc-Karim Bendiane
AU - Anne-Laure Couderc
PY - 2026
DA - 2026/09/21
JO - The Journals of Gerontology Series A: Biological Sciences and Medical Sciences
DO - 10.1093/gerona/glag238
UR - https://doi.org/10.1093/gerona/glag238
AB - Background: Population aging is leading to increased demand for health and long-term care. Artificial intelligence (AI)-based assistive technologies have been developed to support autonomy and care for older adults, yet real‑world adoption remains limited. We review evidence on acceptance and determinants of acceptance of AI‑based assistive technologies among older adults. Methods: We conducted a scoping review using the Joanna Briggs Institute methodology and followed Preferred Reporting Items for Systematic reviews and Meta-Analyses‑Scoping Review guidelines for reporting. Four databases (PubMed, Embase, Web of Science, Cochrane) were searched for studies published from January 2010 to June 2025, initially in any language. We included studies examining acceptance and determinants of acceptance of AI-based assistive technologies among adults aged 65 years and older. We synthesized data using inductive thematic analysis. Results: Twenty‑nine studies met the inclusion criteria, with over one-third conducted in Europe (n = 10). Overall attitudes toward AI‑based assistive technologies were generally favorable when technologies were perceived as useful, easy to use, and tailored to needs. However, actual use was low. Acceptance was associated with socio‑demographic (eg, age, residence), medical (eg, frailty, self‑rated health), psychosocial (eg, stigma), and structural (eg, cost, accessibility) determinants, as well as with institutional and relational factors (eg, preservation of human contact). Conclusions: While older adults have generally positive views of AI‑based assistive technologies, adoption is slow because of intersecting psychosocial, economic, technical, and organizational barriers. User‑centered design, care-led innovation approaches, and implementation strategies that complement—rather than replace—human support may facilitate uptake and sustained use. Development of standardized assessment tools is needed to enable cross-study comparability and guide future research.
SN - 1079-5006
SN - 1758-535X
ER -