BACKGROUND: Socially assistive robots (SARs) hold promise for supporting older adults (OAs) in hospital settings by promoting social engagement, reducing loneliness, and enhancing emotional well-being. They may also assist health care professionals by delivering information, managing routines, and alleviating workload. However, their acceptability and usability remain major challenges, particularly in dynamic real-world care environments. OBJECTIVE: This study aimed to evaluate the acceptability and usability of a SAR in a geriatric day care hospital (DCH) and to identify key factors influencing its adoption by OAs and their informal caregivers. METHODS: Over the course of 1 year, 97 participants (n=65, 67%, OA patients and n=32, 33%, informal caregivers) took part in a mixed methods evaluation of ARI, a socially assistive humanoid robot developed by PAL Robotics. ARI was deployed in the waiting area of a geriatric day care robot in Paris (France), where it interacted with users through voice-based dialogue. After each session, participants completed 2 standardized assessments, the Acceptability E-scale (AES) and the System Usability Scale (SUS), administered orally to ensure accessibility. Open-ended qualitative feedback was also collected to capture subjective experiences and contextual perceptions. RESULTS: Acceptability scores significantly increased across waves (wave 1: mean 15.4/30, SD 5.81; wave 2: mean 20.9/30, SD 5.25; wave 3: mean 22.5/30, SD 4.23; P<.001). Usability scores also improved (wave 1: mean 47.9/100, SD 24.18; wave 2: mean 57.4/100, SD 22.46; wave 3: mean 69.3/100, SD 16.03; P<.001). A strong positive correlation was observed between acceptability and usability scores (r=0.664, P<.001). Qualitative findings indicated improved ease of use, clarity, and user satisfaction over time, particularly following the integration of a large language model (LLM) in wave 2, leading to more coherent, natural, and context-aware interactions. CONCLUSIONS: Successive system enhancements, most notably the integration of an LLM, led to measurable gains in usability and acceptability among patients and informal caregivers. These findings underscore the importance of iterative, user-centered design in deploying SARs in geriatric care environments. TRIAL REGISTRATION: Approved by the French national ethics committee (CPP Ouest II, IRB: 2021/20) as it did not involve randomization or clinical intervention.

Assistance Publique Hôpitaux de Paris Hôpital Broca Centre Mémoire de Ressources et Recherches Île de France Broca Service gériatrie 1 and 2 Paris France

Broca Living Lab Hôpital Broca Paris France

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Prague Czech Republic

Department of Information Engineering and Computer Science University of Trento Trento Italy

ERM Automatismes Carpentras France

Faculty of Engineering Bar Ilan University Bar Ilan Israel

Institut national de la santé et de la recherche médicale Optimisation thérapeutique en pharmacologie OTEN U1144 Université Paris Cité Paris France

Institut national de recherche en sciences et technologies du numérique de l'Université Grenoble Alpes Grenoble France

Interaction Lab Mathematical and Computer Sciences Heriot Watt University Edinburgh United Kingdom

PAL Robotics Barcelona Spain

Zobrazit více v PubMed

United Nations Department of Economic and Social Affairs, Population Division World population ageing 2020 highlights. United Nations. 2020. [2025-04-16]. https://www.un.org/development/desa/pd/news/world-population-ageing-2020-highlights .

Ageing and health. World Health Organization. 2023. [2025-04-16]. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health .

Health at a glance: Europe 2020. OECD. 2020. [2025-04-16]. https://www.oecd.org/en/publications/health-at-a-glance-europe-2020_82129230-en.html .

Chu L, Chen H, Cheng P, Ho P, Weng I, Yang P, Chien S, Tu Y, Yang C, Wang T, Fung H, Yeh S. Identifying features that enhance older adults’ acceptance of robots: a mixed methods study. Gerontology. 2019 Mar 7;65(4):441–450. doi: 10.1159/000494881.000494881 PubMed DOI

Naneva S, Sarda Gou M, Webb TL, Prescott TJ. A systematic review of attitudes, anxiety, acceptance, and trust towards social robots. Int J Soc Robot. 2020 Jun 03;12(6):1179–1201. doi: 10.1007/s12369-020-00659-4. DOI

Dautenhahn K. Socially intelligent robots: dimensions of human-robot interaction. Philos Trans R Soc Lond B Biol Sci. 2007 Apr 29;362(1480):679–704. doi: 10.1098/rstb.2006.2004. R27PVV61W32867J6 PubMed DOI PMC

Mataric M. Socially assistive robotics: human-robot interaction methods for creating robots that care. 9th ACM/IEEE International Conference on Human-Robot Interaction; March 3-6, 2014; Bielefeld, Germany. 2014. p. 333. DOI

Saadatzi MN, Logsdon MC, Abubakar S, Das S, Jankoski P, Mitchell H, Chlebowy D, Popa DO. Acceptability of using a robotic nursing assistant in health care environments: experimental pilot study. J Med Internet Res. 2020 Nov 12;22(11):e17509. doi: 10.2196/17509. v22i11e17509 PubMed DOI PMC

David D, Thérouanne P, Milhabet I. The acceptability of social robots: a scoping review of the recent literature. Comput Hum Behav. 2022 Dec;137:107419. doi: 10.1016/j.chb.2022.107419. DOI

de Jong C, Kühne R, Peter J, van Straten CL, Barco A. Intentional acceptance of social robots: development and validation of a self-report measure for children. Int J Hum-Comput Stud. 2020 Jul;139:102426. doi: 10.1016/j.ijhcs.2020.102426. DOI

Mishra N. Exploring potential and acceptance of socially intelligent robot. In: Thalmann NM, Zhang JJ, Ramanathan M, Thalmann D, editors. Intelligent Scene Modeling and Human-Computer Interaction. Cham: Springer International Publishing; 2021. pp. 259–282.

Martín Rico F, Rodríguez-Lera FJ, Ginés Clavero J, Guerrero-Higueras ÁM, Matellán Olivera V. An acceptance test for assistive robots. Sensors (Basel) 2020 Jul 14;20(14):3912. doi: 10.3390/s20143912. s20143912 PubMed DOI PMC

Holden RJ. A simplified System Usability Scale (SUS) for cognitively impaired and older adult. Proc Int Symp Hum Factors Ergon Health Care. 2020 Sep 16;9(1):180–182. doi: 10.1177/2327857920091021. DOI

Bevilacqua R. Robot-Era project: preliminary results on the system usability. In: Marcus A, editor. Design, User Experience, and Usability: Interactive Experience Design. Cham: Springer International Publishing; 2015. pp. 553–561.

Bishop L, van Maris A, Dogramadzi S, Zook N. Social robots: the influence of human and robot characteristics on acceptance. Paladyn J Behav Robot. 2019;10(1):346–358. doi: 10.1515/pjbr-2019-0028. DOI

de Graaf MM, Ben Allouch S. Exploring influencing variables for the acceptance of social robots. Robot Auton Syst. 2013 Dec;61(12):1476–1486. doi: 10.1016/j.robot.2013.07.007. DOI

Eyssel F, Kuchenbrandt D, Bobinger S. Effects of anticipated human-robot interaction and predictability of robot behavior on perceptions of anthropomorphism. HRI '11: 6th International Conference on Human-Robot Interaction; March 6-9, 2011; Lausanne, Switzerland. 2011. DOI

Heerink M. The influence of a robot’s social abilities on acceptance by elderly users. 15th IEEE International Symposium on Robot and Human Interactive Communication; September 6-8, 2006; Hatfield, UK. 2006. p. 521. DOI

Whelan S, Murphy K, Barrett E, Krusche C, Santorelli A, Casey D. Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: a literature review. Int J of Soc Robot. 2018 Mar 1;10(5):643–668. doi: 10.1007/s12369-018-0471-x. DOI

Mahmoudi Asl A, Toribio-Guzmán JM, Castro-González Á, Malfaz M, Salichs MA, Franco Martín M. Evaluating the user experience and usability of the MINI robot for elderly adults with mild dementia and mild cognitive impairment: insights and recommendations. Sensors (Basel) 2024 Nov 08;24(22):7180. doi: 10.3390/s24227180. s24227180 PubMed DOI PMC

Beuscher LM, Fan J, Sarkar N, Dietrich MS, Newhouse PA, Miller KF, Mion LC. Socially assistive robots: measuring older adults’ perceptions. J Gerontol Nurs. 2017 Dec 01;43(12):35–43. doi: 10.3928/00989134-20170707-04. PubMed DOI PMC

Granata C, Pino M, Legouverneur G, Vidal J, Bidaud P, Rigaud A. Robot services for elderly with cognitive impairment: testing usability of graphical user interfaces. THC. 2013 Jun 01;21(3):217–231. doi: 10.3233/thc-130718. PubMed DOI

Jakob D, Wilhelm S, Gerl A, Ahrens D, Wahl F. Adapting voice assistant technology for older adults: a comprehensive study on usability, learning patterns, and acceptance. Digital. 2025 Jan 31;5(1):4. doi: 10.3390/digital5010004. DOI

Hanschmann L. A LLM-based social robot for human-like sales conversations. In: Følstad A, Araujo T, Papadopoulos S, Law ELC, Luger E, Goodwin M, Hobert S, Brandtzaeg PB, editors. Chatbot Research and Design. Cham: Springer Nature Switzerland; 2024. pp. 61–76.

Atuhurra J. Leveraging large language models in human-robot interaction: a critical analysis of potential and pitfalls. arXiv. Preprint posted online 2024. [doi: 10.48550/arXiv.2405.00693] doi: 10.48550/arXiv.2405.00693. DOI

Kim C. Understanding large-language model (LLM)-powered human-robot interaction. HRI '24: 2024 ACM/IEEE International Conference on Human-Robot Interaction; March 11-15, 2024; Boulder, CO. 2024. DOI

Shibi S, Zaidi S. STEM approach to enhance robot-human interaction through AI large language modelsreinforcement learning. 2024 IEEE Integrated STEM Education Conference (ISEC); March 9, 2024; Princeton, NJ. 2024. DOI

Sobrín-Hidalgo D, González-Santamarta MA, Guerrero-Higueras AM, Rodríguez-Lera FJ, Matellán-Olivera V. Explaining autonomy: enhancing human-robot interaction through explanation generation with large language models. arXiv. Preprint posted online 2024. [doi: 10.48550/arXiv.2402.04206] doi: 10.48550/arXiv.2402.04206. DOI

Zhang B, Soh H. Large language models as zero-shot human models for human-robot interaction. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); October 1-5, 2023; Detroit, MI. 2023. pp. 7961–7968. DOI

Zhang C, Chen J, Li J, Peng Y, Mao Z. Large language models for human–robot interaction: a review. Biomim Intell Robot. 2023 Dec;3(4):100131. doi: 10.1016/j.birob.2023.100131. DOI

Liberman-Pincu E, Oron-Gilad T. Exploring the effect of mass customization on user acceptance of socially assistive robots (SARs). HRI '22: 2022 17th ACM/IEEE International Conference on Human-Robot Interaction; March 7-10, 2022; Sapporo, Hokkaido. 2022. pp. 880–884. DOI

Irfan B, Kuoppamäki S, Hosseini A, Skantze G. Between reality and delusion: challenges of applying large language models to companion robots for open-domain dialogues with older adults. Auton Robot. 2025 Mar 10;49(1):9. doi: 10.1007/s10514-025-10190-y. DOI

Lu H, Chan SS, Fung AW, Lam LC. Utility of Montreal Cognitive Assessment (Hong Kong Version) in the Diagnosis of Mild Neurocognitive Disorders (NCD): NCD due to Alzheimer Disease (NCD-AD) and NCD due to Vascular Disease (NCD-Vascular) J Am Med Dir Assoc. 2016 Apr 01;17(4):366–7.S1525-8610(16)00038-4 PubMed

Fasola J, Mataric M. A socially assistive robot exercise coach for the elderly. J Hum Robot Interact. 2013;2(2):3–32. doi: 10.5898/jhri.2.2.fasola. DOI

Heerink M, Kröse B, Evers V, Wielinga B. Assessing acceptance of assistive social agent technology by older adults: the Almere model. Int J of Soc Robot. 2010 Sep 4;2(4):361–375. doi: 10.1007/s12369-010-0068-5. DOI

Becchimanzi C, Iacono E, Brischetto A. Acceptability of assistive robotics by older adults: results from a human-centred qualitative study. AHFE 2022 International Conference on Human Factors in Accessibility and Assistive Technology; July 24-28, 2022; New York, NY. 2022. p. 37. DOI

Olde Keizer RACM, van Velsen L, Moncharmont M, Riche B, Ammour N, Del Signore S, Zia G, Hermens H, N’Dja A. Using socially assistive robots for monitoring and preventing frailty among older adults: a study on usability and user experience challenges. Health Technol. 2019 Apr 9;9(4):595–605. doi: 10.1007/s12553-019-00320-9. DOI

Olatunji S, Potenza A, Oron-Gilad T, Kiselev A, Loutfi A, Edan Y. Usability testing for the operation of a mobile robotic telepresence system by older adults. Proc Hum Factors Ergon Soc Annu Meet. 2021 Feb 09;64(1):1191–1195. doi: 10.1177/1071181320641284. DOI

Jones-Jang S, Park Y. How do people react to AI failure? Automation bias, algorithmic aversion, and perceived controllability. J Comput Mediat Commun. 2022;28(1):zmac029. doi: 10.1093/jcmc/zmac029. DOI

Wada K, Shibata T. Robot therapy in a care house - its socio-psychological and physiological effects on the residents. 2006 IEEE International Conference on Robotics and Automation; May 15-19, 2006; Orlando, FL. 2006. pp. 3966–3971. DOI

Almeida F. Strategies to perform a mixed methods study. Eur J Educ Stud. 2018;5(1):137–151. doi: 10.5281/zenodo.1406214. DOI

Pluye P, Hong QN. Combining the power of stories and the power of numbers: mixed methods research and mixed studies reviews. Annu Rev Public Health. 2014 Mar;35:29–45. doi: 10.1146/annurev-publhealth-032013-182440. PubMed DOI

Curry LA, Krumholz HM, O'Cathain A, Plano Clark VL, Cherlin E, Bradley EH. Mixed methods in biomedical and health services research. Circ Cardiovasc Qual Outcomes. 2013 Jan 01;6(1):119–123. doi: 10.1161/CIRCOUTCOMES.112.967885. 6/1/119 PubMed DOI PMC

Scammon D, Tomoaia-Cotisel A, Day RL, Day J, Kim J, Waitzman NJ, Farrell TW, Magill MK. Connecting the dots and merging meaning: using mixed methods to study primary care delivery transformation. Health Serv Res. 2013 Dec;48(6 Pt 2):2181–2207. doi: 10.1111/1475-6773.12114. PubMed DOI PMC

Shorten A, Smith J. Mixed methods research: expanding the evidence base. Evid Based Nurs. 2017 Jul 14;20(3):74–75. doi: 10.1136/eb-2017-102699. eb-2017-102699 PubMed DOI

Terrell S. Mixed-methods research methodologies. TQR. 2015 Jan 20;17:254–265. doi: 10.46743/2160-3715/2012.1819. DOI

McKim C. The value of mixed methods research: a mixed methods study. J Mixed Methods Res. 2016 Jul 08;11(2):202–222. doi: 10.1177/1558689815607096. DOI

ARI. Pal Robotics. [2025-07-22]. https://pal-robotics.com/robot/ari/

Alameda-Pineda X, Addlesee A, Hernández García D, Reinke C, Arias S, Arrigoni F, Auternaud A. Socially pertinent robots in gerontological healthcare. arXiv. Preprint posted online 2024. [doi: 10.48550/arXiv.2404.07560] doi: 10.48550/arXiv.2404.07560. DOI

Gunson N, Hernández García D, Sieinska W, Dondrup C, Lemon O. Developing a social conversational robot for the hospital waiting room. 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); August 29-September 2, 2022; Naples, Italy. 2022. DOI

Wei-Lin C, Zhuohan L, Zi L, Ying S, Zhanghao W, Hao Z, Lianmin Z, Siyuan Z, Yong-hao Z, Joseph E. Vicuna: an open-source chatbot impressing GPT-4 with 90%* ChatGPT quality. LMSYS. 2023. [2025-07-22]. https://lmsys.org/blog/2023-03-30-vicuna/

Micoulaud-Franchi J, Sauteraud A, Olive J, Sagaspe P, Bioulac S, Philip P. Validation of the French version of the Acceptability E-scale (AES) for mental E-health systems. Psychiatry Res. 2016 Mar 30;237:196–200. doi: 10.1016/j.psychres.2016.01.043.S0165-1781(15)30651-X PubMed DOI

Bangor A, Kortum PT, Miller JT. An empirical evaluation of the System Usability Scale. Int J Hum-Comput Interact. 2008 Jul 30;24(6):574–594. doi: 10.1080/10447310802205776. DOI

Brooke J. SUS: a quickdirty usability scale. In: Jordan PW, Thomas B, Weerdmeester BA, McClelland IL, editors. Usability Evaluation in Industry. London, UK: Taylor & Francis; 1996. pp. 189–194.

Shackel B. What is usability?. 4th International Conference on Human-Computer Interaction; 1991; Stuttgart, Germany. 1991. pp. 3–15. DOI

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006 Jan;3(2):77–101. doi: 10.1191/1478088706qp063oa. DOI

SPRING D1.6 – user feedback from the final validation in relevant environments. SPRING Consortium. 2024. [2025-07-22]. https://spring-h2020.eu/wp-content/uploads/2024/07/SPRING_D1.6_-User-feedback-from-the-final-validation-relevant-environments_vFinal_31.05.2024.pdf .

Socially pertinent robots in gerontological healthcare. SPRING. 2025. [2025-07-22]. https://spring-h2020.eu/

Mielke C. Towards explainable spoken dialogue systems for robots. 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations; July 5-10, 2020; Online. 2020. pp. 234–241. DOI

Chen C, Lifset ET, Han Y, Roy A, Hogarth M, Moore AA, Farcas E, Weibel N. Screen or no screen? Lessons learnt from a real-world deployment study of using voice assistants with and without touchscreen for older adults. ASSETS '23: 25th International ACM SIGACCESS Conference on Computers and Accessibility; October 22-25, 2023; New York, NY. 2023. Oct, pp. 1–21. PubMed DOI PMC

Petit E. The human/robot empathic relationship: normal or pathological? Implications Philosophiques. 2021. Jul 21, [2005-07-22]. https://tinyurl.com/3ptcasyk .

Olatunji A. Investigating elderly perceptions of explainable robotic agents in elder care settings. Workshop on Modeling Robots for Aging and Eldercare (MoRoBAE); 2023; Stockholm, Sweden. 2023.

Yamaji Y. How predictability of robot behaviors affects users’ acceptance and trust in HRI: a study of timing control strategies in human-robot collaboration. IEEE Trans Haptics. 2021;14(4):708–719. doi: 10.1109/TOH.2021.3116294. DOI

Klaassen W, van Dijk B, Spruit M. A review of challenges in speech-based conversational AI for elderly care. arXiv. Preprint posted online 2024. [doi: 10.48550/arXiv.2412.07388] doi: 10.48550/arXiv.2412.07388. PubMed DOI

Flandorfer P. Population ageing and socially assistive robots for elderly persons: the importance of sociodemographic factors for user acceptance. Int J Populat Res. 2012;2012:829835. doi: 10.1155/2012/829835. DOI

Borghouts Judith, Eikey Elizabeth V, De Leon Cinthia, Schueller Stephen M, Schneider Margaret, Stadnick Nicole A, Zheng Kai, Wilson Lorraine, Caro Damaris, Mukamel Dana B, Sorkin Dara H. Understanding the Role of Support in Digital Mental Health Programs With Older Adults: Users' Perspective and Mixed Methods Study. JMIR Form Res. 2022 Dec 13;6(12):e43192–74. doi: 10.2196/43192. v6i12e43192 PubMed DOI PMC