# Patient-Led Smartwatch ECG Follow-Up Strategy After AF Ablation: Clinical Trial Design and Implementation

## TL;DR

This study shows how patients can use smartwatches to monitor their heart rhythm after AF ablation, improving follow-up care and data collection.

## Contribution

The paper introduces a structured, patient-led smartwatch ECG workflow integrated into clinical trials and routine post-ablation care.

## Key findings

- Patients using smartwatches recorded a median of 170 ECGs over 12 months with high engagement.
- Symptom-annotated ECGs were significantly more likely to show AF compared to unannotated ones.
- Smartwatch-derived rhythm classification showed high accuracy with positive predictive values of 0.96 for AF and 0.95 for sinus rhythm.

## Abstract

Conventional follow-up after atrial fibrillation (AF) catheter ablation relies on physician-led interval monitoring and often fails to characterize paroxysmal symptoms. An increasing number of patients use smartwatch-based ECG devices for rhythm monitoring, but their structured integration into clinical workflows and the handling of the resultant data are not well described.

To describe the design, operationalization, data pipeline, and user engagement of a patient-led smartwatch ECG follow-up strategy after AF ablation within a randomized clinical trial.

A prospective, randomized controlled trial of adults undergoing first-time AF ablation was conducted. Participants were randomized to an Apple Watch-based protocol (daily and symptom-triggered ECGs) or standard follow-up. A prespecified audit of the smartwatch-derived rhythm classification was conducted. User engagement, symptom annotation, and downstream resource use were quantified. Primary clinical outcomes are reported in a companion Brief Report.

Of the 168 enrolled participants (mean age 60.5 ± 9.9 years, 52 (31.0%) female, 84 (50.0%) persistent AF), Active-arm participants recorded a median of 170 (IQR 93–380) ECGs over 12 months and transmitted a median of 1.9% (0.0–8.3) for review. Symptom-annotated ECGs were more likely to show AF compared with unannotated ECGs (OR 16.1, 95% CI 13.0–19.9, P < 0.001) Watch-derived AF and sinus rhythm labels had positive predictive values of 0.96 and 0.95 respectively, although one-third of ECGs were unclassified.

A structured, patient-led smartwatch ECG workflow can be embedded into routine post-ablation care with high engagement, modest staff workload, and accurate device-level rhythm classification. This implementation framework provides a practical template for integrating patient-generated wearable data into AF follow-up pathways and future digitally enabled trials.