Currently submitted to: JMIR Aging
Date Submitted: Jun 5, 2026
Open Peer Review Period: Jun 6, 2026 - Aug 1, 2026
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Understanding Home Fall Risk and Acceptance of AI-enabled Assessment Tools: A Qualitative Descriptive Study
ABSTRACT
Background:
Falls among older adults are a major public health concern, with many risks arising from environmental conditions within the home. Although home safety assessments are effective, they are often limited by accessibility and scalability. Artificial intelligence (AI)–enabled tools have the potential to support fall risk assessment; however, their successful implementation depends on user acceptance and alignment with the needs of older adults, caregivers, and health care providers.
Objective:
This study aimed to explore perceptions of home fall risk and examine the acceptability of AI-enabled fall risk assessment tools among older adults, informal caregivers, and health care providers.
Methods:
A qualitative descriptive study was conducted using inductive thematic analysis. Participants were recruited through online platforms and snowball sampling. Semi-structured interviews were conducted with 21 participants (8 older adults, 4 caregivers, 9 providers) to explore experiences with fall-related home environments, perceptions of fall risk, and views on AI-enabled assessment tools.
Results:
Three interrelated themes emerged regarding home fall-risk experiences and AI acceptance: 1) Fall risk was embedded within everyday home environments, where hazards such as clutter, poor lighting, and layout were often normalized within daily routines rather than perceived as modifiable risks. 2) Fall prevention was characterized as an ongoing negotiation between independence and safety, with older adults prioritizing autonomy while caregivers and providers emphasized risk reduction. 3) Participants expressed openness to AI-enabled tools; however, acceptance was conditional and dependent on perceived usefulness, ease of use, accuracy, and alignment with daily needs. Concerns regarding privacy, reliability, and the ability of AI to capture contextual factors were prominent. Participants emphasized that AI should complement, rather than replace, human caregiving and clinical judgment.
Conclusions:
Successful implementation will require balancing safety with autonomy, integrating tools into existing care practices, and fostering trust through transparency, usability, and human oversight. These insights provide important guidance for the development and implementation of scalable, AI-enabled fall prevention strategies to support aging in place. Clinical Trial: N/A
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