Identifying Features that Enhance Older Adults’ Acceptance of Robots: A Mixed Methods Study

Chu, Li; Chen, Hung−Wen; Cheng, Pei-Yi; Ho, Pokuan; Weng, I. C.; Yang, Pei‐Ling; Chien, Shu‐Hua; Tu, Yun-Chen; Yang, Chien-Chun; Wang, Te-Mei; Fung, Helene H.; Yeh, Su‐Ling

doi:10.1159/000494881

Cited by 52 publications

(49 citation statements)

References 38 publications

(50 reference statements)

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“…Contrary to expectations, older people seem to accept and respond well to the use of these novel enabling technologies for remote assistance, social communication and medical care, highlighting the importance of incorporating their needs and preferences in developing effective technological solutions. In addition, when faced with a choice, they seem to prefer a service-oriented robot over the more companion-oriented robot [39]. https://doi.org/10.29328/journal.apmh.1001011…”

Section: Assisted Technology In Geriatric Care and Rehabilitation: Romentioning

confidence: 99%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

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Section: Assisted Technology In Geriatric Care and Rehabilitation: Romentioning

confidence: 99%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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“…The elderly's trust in smart elderly care mainly involves privacy security trust functional trust, service trust, and device reliability trust [51]. The concept of trust perception C4 is derived from the Integrated Technology Acceptance Model.…”

Section: Steps To Calculate Importance Based On Neighborhood Rough Setmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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“…Assistive robots can be further classified into two types [20]: (1) service robots like a bearlike nurse robot called Robear that can lift patients out of bed, and (2) companion robots such as Paro, a baby harp seal robot that can improve social interactions of older adults with each other and with caregivers at a care house [21]. Chu et al [22] demonstrated that older adults anticipated robots to do housework; that is, a robot comes with service functions including finding or fetching items, helping them with running errands, making meals, and so on. Similarly, Ezer et al [23] also found that older adults needed robots to help them do chores, ensure security, and provide other assistance.…”

Section: Introductionmentioning

confidence: 99%

Age-Related Differences in the Uncanny Valley Effect

Chien

Yeh

2020

Gerontology

Self Cite

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Background: Due to declining birthrates and an increasing aging population, shortage of the caregiving labor force has become a global issue. Among various efforts toward the solution, introducing robotic products for assistance could provide an effective way to help older adults in their daily lives. As previous studies have indicated that older adults' acceptance of robots is lower than that of younger adults, enhancing older adults' acceptance of robots is imperative. Because older adults' first impressions based on a robot's appearance might affect their acceptance of the robot, we investigated the uncanny valley effect (UVE) here. The UVE refers to the phenomenon that people rate robots more positively as robots become more humanlike, but only up to a certain point; as robots approach a near-perfect similarity to human appearance, likeability drops and forms the uncanny valley. Nevertheless, evidence for the UVE came mainly from younger adults. Objective: The present study aimed to examine whether the UVE varies across different age groups and whether a robot's appearance would affect participants' acceptance of the robot's service or companionship. Methods: An online questionnaire study was conducted with 255 participants, including younger (n = 77, age 18-39 years), middle-aged (n = 87, age 40-59 years), and older (n = 91, age 60-87 years) adults. Participants were asked to view each picture in a set selected from a total of 83 robot pictures and evaluate their impressions of each robot and the intention of use regarding robot function as a service provider or a companion. Results: The UVE was found in younger and middle-aged adults; however, older adults did not show the UVE. Older adults preferred humanlike over non-humanlike robots, regardless of robot function. Conclusion: The design of assistive robots should take the UVE into consideration by customizing robot appearance based on the age group of the intended user.

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Identifying Features that Enhance Older Adults’ Acceptance of Robots: A Mixed Methods Study

Cited by 52 publications

References 38 publications

Artificial Intelligence in the healthcare of older people

Artificial Intelligence in the healthcare of older people

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Age-Related Differences in the Uncanny Valley Effect

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