Patients with acute decompensated heart failure (HF) have a significantly high risk of death and HF re-hospitalization during the vulnerable phase post discharge. Therefore, early post-discharge management is crucial, and the cornerstone of HF treatment-particularly for HF with reduced ejection fraction (HFrEF)-is guideline-directed medical therapy (GDMT), a comprehensive pharmacotherapeutic strategy supported by robust clinical evidence. Timely titration of GDMT, especially within the first few weeks after discharge, has been shown to improve clinical outcomes, reduce readmissions, and enhance long-term prognosis. However, ensuring optimal follow-up and therapeutic adjustments remains a major challenge in real-world practice. Atrial fibrillation (AF) is a common comorbidity in patients with HF, especially in those with severe HF. The presence of AF significantly complicates the clinical course and worsens the prognosis of HF. Advances in wearable technology have made continuous, non-invasive monitoring of vital signs, arrhythmia burden, and physical status increasingly feasible. Devices such as smartwatches and ECG belts can provide real-time physiological data, offering new opportunities for remote and proactive disease management. Despite the growing availability of such data, the complex interplay between AF and HF demands highly personalized management. Currently, there is a lack of high-quality clinical evidence on how to effectively integrate wearable device data into personalized strategies for this specific patient population. This is an open-label, multi-center, endpoint-blinded, parallel-group randomized clinical trial supported by the American Heart Association. The primary objective is to determine whether wearable device-assisted digital consultations can optimize GDMT in patients with AF complicated by acute decompensated HF. The study plans to enroll 400 participants, who will be randomly assigned to either a wearable device-assisted intervention group or a conventional treatment control group. The primary endpoint is the change in HF GDMT score 3 months after randomization. Apple Inc. provided funding, devices, and technical support for this study. Apple was not a sponsor of the trial and was not involved in its execution, data analysis, interpretation, or manuscript preparation.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
400
This intervention comprises two core components: (A) A data collecting system via smartwatch and ECG belt; and (B) A decision-support algorithms for risk stratification and medication dose adjustment based on real-time data. Participants will be instructed to wear both the Apple Watch and ECG belt for a minimum of 12-18 hours per day to monitor parameters, such as heart rhythm and respiratory rate. Participants will be prompted via the research APP to complete questionnaires for symptom, blood pressure, and body weight data. All collected data are automatically uploaded to a secure cloud-based platform. A centralized decision-support system analyzes the integrated information using a suite of predefined, multi-layered algorithms to identify patients requiring intervention. Patients will be stratified into three tiers based on integrated physiological metrics and symptom reports: Normal subgroup, Abnormal subgroup, and E-alarm subgroup.
Changes in Heart Failure Guideline-directed Medical Therapy (GDMT) Score
High score favorable
Time frame: 3 Months After Randomization
AF recurrence monitored by Apple Watch and ECG belt
Time frame: 3 Months After Randomization
AF burden monitored by ECG belt
Time frame: 12 Months After Randomization
Time to first HF hospitalization and/or cardiovascular death
Time frame: 12 Months After Randomization
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