AI-Enabled Frailty Risk Prediction in Adult Congenital Heart Disease

Precision Risk Prediction of Symptom Clusters and Frailty With AI-Guided Emotion Detection: A Three-Year Study in Adults With Congenital Heart Disease

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)

Inclusion Criteria:

1. Adults aged 20 years or older.
2. Diagnosed with congenital heart disease by a physician.
3. Able to communicate in Mandarin or Taiwanese, with normal cognitive function.

Exclusion Criteria:

1. Presence of other congenital anomalies.
2. Currently hospitalized for acute decompensation, or having severe cognitive impairment or psychiatric symptoms that preclude completion of questionnaires.
3. Pregnant individuals.