N-UNRAQ: Psychometric testing of the modified nursing Users’ Needs, Requirements, and Abilities Questionnaire for care robots in long-term care

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Authors: Katie Trainum, Karen Johnson, Bo Xie, Elizabeth Heitkemper, Elliott Hauser
Cited by: 2
References: 40

Abstract

Introduction: As care robots become increasingly common in long-term care (LTC), understanding the perceptions of those who provide patient care is critical to ensure technology aligns with workforce needs and enhances care. Methods: This cross-sectional study aimed to modify the Users' Needs, Requirements, and Abilities Questionnaire (UNRAQ) for LTC nursing staff in the United States and conduct initial psychometric testing. The modified instrument (N-UNRAQ) expands the original assistive role and social aspects domains into three subdomains (facility, staff, robot) and adds three LTC-specific assistive tasks (transferring/lifting, administering medications, and assisting with activities of daily living). Electronic surveys were distributed to nursing staff across a random sample of LTC facilities in Central Texas. Results: Responses from 122 staff across 28 LTC facilities were analyzed to assess reliability and validity. Results demonstrated excellent internal consistency across domains and the full questionnaire, with partial support for construct validity. Findings suggest that technological experience, rather than prior robot familiarity, influenced perceptions. Limited response variability in facility and staff domains suggested a ceiling effect that may constrain discrimination. Participants rated assistive and social tasks as important to their roles and facilities but expressed neutral attitudes toward robots performing them. Environmental monitoring and reminders were rated most favorably, while physical assistance tasks were rated less positively, suggesting staff are more comfortable with indirect rather than hands-on robot support. Conclusions: The N-UNRAQ offers a reliable tool for assessing nursing staff perceptions of care robots. Future work should refine the instrument and validate it across broader contexts.

Fields of Study & Subjects

Social Robot Interaction and HRI AI in Service Interactions Healthcare Technology and Patient Monitoring

Publication details

Journal / source
Digital Health
Publisher
SAGE Publications
Volume
12
Pages
20552076261425494–20552076261425494
Publication date
1 February 2026
Language
EN