Evidence map›Paper›PMID 37986104›Full record

ArticleJMIR aging2023

Identifying Preferred Appearance and Functional Requirements of Aged Care Robots Among Older Chinese Immigrants: Cross-Sectional Study.

Ching-Ju Chiu, Yi-Hsuan Lo, Jed Montayre, Hammoda Abu-Odah, Mei-Lan Chen, Ivy Yan Zhao

Abstract read
In one paragraph

Article in JMIR aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ching-Ju ChiuInstitute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID http://orcid.org/0000-0002-2352-9105
Yi-Hsuan LoDepartment of Statistics, College of Management, National Cheng Kung University, Tainan, Taiwan.ORCID http://orcid.org/0000-0001-6379-0714
Jed MontayreWorld Health Organization Collaborating Centre for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.ORCID http://orcid.org/0000-0002-2435-8061
Hammoda Abu-OdahWorld Health Organization Collaborating Centre for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.ORCID http://orcid.org/0000-0002-8874-2599
Mei-Lan ChenSchool of Nursing, Byrdine F Lewis College of Nursing and Health Professions, Georgia State University, Atlanta, GA, United States.ORCID http://orcid.org/0000-0002-0461-1066
Ivy Yan ZhaoWorld Health Organization Collaborating Centre for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.ORCID http://orcid.org/0000-0003-4597-8929

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Older Chinese immigrants constitute the largest older Asian ethnic population in New Zealand. Aging in a foreign land can be complex, presenting increasing challenges for gerontology scholars, practitioners, and policy makers. Older Chinese immigrants are more susceptible to experiencing loneliness and social isolation compared to native older people, primarily due to language, transportation, and cultural barriers. These factors subsequently impact their physical and mental health. With advancements in robotic technology, aged care robots are being applied to support older people with their daily living needs. However, studies on using robots with older immigrants living in the community are sparse. Their preferences for the appearance and function of aged care robots are unclear, which impacts the acceptance and usability of robots, highlighting the need for a user-centered design approach. Objective: This study aims to explore older Chinese immigrants' needs and preferences toward the appearance and function of aged care robots and to examine their relationships with the demographic characteristics of participants. Methods: A cross-sectional design was used in this study, which was undertaken between March and May 2020. A total of 103 participants completed a web-based survey. Results: The average age of participants was 68.7 (SD 5.5) years. The results suggest that 41.7% (n=43) of the 103 participants preferred a humanlike adult appearance, while 32% (n=33) suggested an animallike appearance. These participants reported higher scores in both rigorousness and friendliness compared to others who preferred different robot appearances. Participants expressed a greater preference for the functions of housework assistance (n=86, 83.5%), language translation (n=79, 76.7%), health monitoring (n=78, 75.7%), facial expressions (n=77, 74.8%), news reading (n=66, 64.1%), and security monitoring (n=65, 63.1%). These preferences were found to be significantly associated with marital status, financial status, and duration of immigration. Conclusions: To support immigrant populations to age well in a foreign country and address the growing shortage of health and social professionals, it is important to develop reliable robotic technology services that are tailored based on the needs and preferences of individuals. We collected and compared the perspectives of immigrant and nonimmigrant participants on using robots to support aging in place. The results on users' needs and preferences inform robotic technology services, indicating a need to prioritize older Chinese immigrants' preference toward aged care robots that perform housework assistance, language translation, and health and safety monitoring, and robots with humanlike features.

Indexed as

aged careagingaging in placeappearanceAsianAsian populationChinesecompanioncompanionshipcross sectionalcross-sectionalelderlyfunctionhealthy agingimmigrantisolationolder adultspopulationpopulation studiespreferencerobotroboticroboticsrobotic technology servicesrobotssocialsurvey

Identifiers

PMID37986104
PMCPMC10686207

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.