Abstract:
Background: Frailty is a major contributor to functional decline, reduced quality of life, and increased healthcare utilization among older adults. Mild frailty represents a potentially reversible stage; however, existing home-based interventions are often standardized, resource-intensive, and insufficiently responsive to individual needs. Emerging artificial intelligence (AI) technologies offer new opportunities to enhance personalized care through continuous monitoring, adaptive support, and proactive health management.
This study aims to evaluate the feasibility, acceptability, and preliminary effectiveness of a nurse-led, agentic AI-enabled home frailty management program for community-dwelling older adults with mild frailty.
Methods: A 12-week multicentre pilot randomized controlled trial will recruit 300 community-dwelling older adults with mild frailty (Clinical Frailty Scale = 5).
Participants in the intervention group will receive a nurse-led, AI-enabled home frailty management program integrating wearable monitoring technology, weekly virtual coaching, and an agentic multimodal AI health coach. The AI system will continuously analyze physiological, behavioral, and interaction data collected from wearable devices and participant engagement records to generate personalized recommendations, adaptive goal-setting strategies, motivational support, and early risk alerts. A dynamic digital health profile will be maintained for each participant to facilitate context-aware and individualized intervention delivery. Weekly nurse-led consultations will complement AI-generated insights through a human–AI collaborative care model. Participants in the control group will receive usual home health services.
The primary outcome is functional independence measured by the Barthel Index. Secondary outcomes include physical performance, health-related quality of life, intervention engagement, acceptability, and implementation outcomes assessed through qualitative interviews.
Results: Recruitment is currently underway. The study will generate preliminary evidence regarding feasibility, participant engagement, acceptability, and potential impacts on functional outcomes among mildly frail older adults.
Conclusions: This study addresses an important gap in early frailty management by integrating nurse-led care with agentic AI-driven personalized support. Findings will inform the development and implementation of scalable digital nursing models that promote healthy aging, support independent living, and advance the integration of AI into nursing practice.
Biography:
Jiayi Xu is a PhD student at the School of Nursing, Sun Yat-sen University, China. Her research focuses on chronic disease management, nursing education, and digital health innovation. She has published nine papers in JCR Q1 journals, including three first-author publications in international peer-reviewed journals such as BMC Nursing. She has participated in international academic conferences, including the Sigma Nursing Conference. Her current research interests include AI-enabled nursing interventions, healthy ageing, and innovative models of care for older adults and individuals with chronic conditions.
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