Evidence map›Paper›PMID 42443873›Full record

ArticleBMC palliative care2026

The role of an educational robot in advance decision learning among community-dwelling older adults.

Chiu-Mieh Huang, Jia-Yi Yang, Su-Fei Huang, Hsiao-Pei Hsu, Yi-Chun Shieh, Li-Ting Lu, Jong-Long Guo

Abstract read
In one paragraph

Article in BMC palliative care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Chiu-Mieh HuangInstitute of Clinical Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Jia-Yi YangDepartment of Health Promotion and Health Education, College of Education, National Taiwan Normal University, No. 162, Section 1, Heping E. Rd., Taipei, 10610, Taiwan.
Su-Fei HuangDepartment of Intelligent Technology and Long-Term Care, MacKay Junior College of Medicine, Nursing, and Management, Taipei, Taiwan.
Hsiao-Pei HsuInstitute of Clinical Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yi-Chun ShiehInstitute of Community Health Care, College of Nursing, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Li-Ting LuDepartment of Nursing, University of Kang Ning, Taipei, Taiwan.
Jong-Long GuoDepartment of Health Promotion and Health Education, College of Education, National Taiwan Normal University, No. 162, Section 1, Heping E. Rd., Taipei, 10610, Taiwan. jonglong@ntnu.edu.tw.ORCID http://orcid.org/0000-0002-3120-014X

Funding

National Science and Technology Council, Taiwan NSTC 113-2622-H-A49-001
6 · The paper itself

Abstract

backgroundAdvance decisions are a key component of advance care planning (ACP) and support individuals in communicating their preferences for future medical care. Despite policy efforts to promote ACP, public awareness and engagement in completing advance decisions remain limited in many settings. Educational robots, which provide interactive and engaging learning experiences, may represent a novel strategy for delivering ACP education in community settings.

aimThis study aimed to examine older adults' acceptance of an educational robot designed to facilitate learning about advance decisions. Specifically, we investigated factors associated with their intention to continue using the robot to learn about advance decisions and their intention to recommend the robot to others for advance decisions education.

methodsA cross-sectional survey was conducted among older adults recruited from community centers in northern Taiwan. After providing informed consent, participants attended a 90-minute robot-assisted educational session introducing key concepts of advance decisions and ACP. Following the session, participants completed a self-administered questionnaire assessing human-computer trust, decisional balance (perceived pros and cons of advance decisions), personal involvement, satisfaction with the robot-assisted learning experience, intention to continue using the educational robot, and intention to recommend the robot to others. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed relationships among study variables.

resultsHuman-computer trust significantly increased older adults' intention to continue using the educational robot, both directly and indirectly through satisfaction with the learning experience. Trust also influenced participants' intention to recommend the robot to others indirectly via satisfaction. Perceived benefits of advance decisions were positively associated with recommendation intention, whereas perceived barriers showed no significant effects. Neither the perceived benefits nor barriers of advance decisions significantly influenced the intention to continue using the robot. Overall, satisfaction emerged as a key mediator linking trust to behavioral intentions related to robot-assisted ACP education.

conclusionsTrust in the technology and satisfaction with the learning experience play critical roles in shaping continued engagement and willingness to recommend such interventions. Integrating interactive technologies into ACP education may help expand public awareness and promote broader engagement in advance care planning.

Indexed as

Advance Care PlanningDecision MakingRoboticsAgedAged, 80 and overCross-Sectional StudiesFemaleHumansIndependent LivingMaleMiddle AgedSurveys and QuestionnairesTaiwanAdvance care planningAdvance directivesDecisional balanceEducational robotsOlder adultsTechnology acceptance

Identifiers

PMID42443873
PMCPMC13644141

What OpenQuestion holds

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Registered trials

None linked

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.