Factors Influencing Caregivers' Intention to Use Transfer-care Robots: A Sequential Explanatory Mixed-methods Study

Area of Science:

• Robotics in Healthcare
• Human-Computer Interaction
• Caregiver Technology Adoption

Background:

• Limited research exists on transfer-care robot (TCR) acceptance across diverse caregiver roles and settings.
• Existing studies often focus on single professional groups or institutional environments.
• A holistic perspective across the continuum of care is needed to understand technology adoption.

Purpose of the Study:

• To examine the acceptance of transfer-care robots (TCRs) among nurses, personal care assistants, and family caregivers.
• To identify key predictors of intention to use TCRs across various care settings (hospitals, long-term care, home care).
• To explore caregiver perspectives on the benefits and requirements for successful TCR implementation.

Main Methods:

• A sequential explanatory mixed-methods design was employed.
• Quantitative survey (n=224) assessing Technology Acceptance Model constructs.
• Qualitative focus group interviews (n=15) to contextualize quantitative findings.

Main Results:

• Job relevance was the strongest predictor of intention to use TCRs (β=0.50, P<.001).
• Perceived usefulness and self-efficacy also significantly predicted intention to use (adj. R²=.81).
• Qualitative themes revealed the physical burden of transfers, need for assistance, and desire for safe, efficient, transformative robotic technology.

Conclusions:

• User-centered design and workflow alignment are essential for successful TCR implementation.
• Addressing caregiver needs and highlighting job relevance can enhance technology adoption.
• Findings support developing informatics strategies for diverse healthcare settings to integrate robotic assistance effectively.