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AI-Enabled Frailty Risk Prediction in Adult Congenital Heart Disease
Sponsor: Mackay Memorial Hospital
Summary
The goal of this three-year mixed-methods observational study with an embedded randomized controlled trial is to develop and validate a frailty risk prediction model and evaluate an artificial intelligence-based voice emotion detection-guided counselling intervention in adults with congenital heart disease (ACHD). The main questions it aims to answer are: Are symptom clusters associated with frailty and psychological outcomes in adults with congenital heart disease? Can symptom clusters and psychosocial factors be used to predict frailty risk over time in ACHD patients? Does an AI-based voice emotion detection-guided counselling intervention improve psychological outcomes, fatigue, and quality of life among high-risk ACHD patients? Researchers will compare ACHD patients receiving AI-based voice emotion detection-guided counselling with those receiving usual care to determine whether the intervention reduces depression, anxiety, sleep disturbance, fatigue, and frailty risk, and improves grit and quality of life. Participants will: Complete longitudinal assessments of symptom clusters, frailty, and psychological status at baseline and follow-up time points Participate in qualitative interviews to explore lived experiences related to symptoms and frailty Receive AI-based voice emotion detection-guided counselling (intervention group only in Year 3)
Official title: Precision Risk Prediction of Symptom Clusters and Frailty With AI-Guided Emotion Detection: A Three-Year Study in Adults With Congenital Heart Disease
Key Details
Gender
All
Age Range
20 Years - Any
Study Type
INTERVENTIONAL
Enrollment
410
Start Date
2026-03-01
Completion Date
2028-12-31
Last Updated
2026-03-18
Healthy Volunteers
No
Conditions
Interventions
AI-Based Voice Emotion Detection-Guided Counselling
Participants assigned to the intervention arm will receive an artificial intelligence-based voice emotion detection-guided counselling intervention in addition to usual care. The intervention uses voice recordings collected during structured counselling sessions to analyze emotional features, including emotional valence and arousal, through artificial intelligence-based voice emotion detection algorithms. Based on the analyzed emotional profiles, individualized psychological feedback and counselling guidance are provided to support emotional regulation, stress coping, and adaptive self-management. The counselling content is tailored to participants' emotional states and symptom experiences and focuses on reducing psychological distress, improving sleep and fatigue management, enhancing grit, and promoting quality of life. The intervention is delivered by trained healthcare professionals following a standardized protocol, with sessions conducted at predefined intervals during the inter
Usual Care (Control)
Usual Care