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

BACKGROUND

Advance 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.

AIM: This 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.

METHODS

A 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.

RESULTS

Human-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.

CONCLUSIONS

Trust 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.