Wearable Smartwatches for Atrial Fibrillation Detection and Burden Estimation After Ablation: Comparison With Continuous Monitoring
Bibliographic record
Abstract
AIMS: Wearable ECG-enabled smartwatches have been validated for atrial fibrillation (AF) screening, but their accuracy for monitoring AF recurrence and quantifying AF burden after catheter ablation is uncertain. To evaluate the simulated performance of three commercial smartwatch algorithms for AF recurrence detection and AF burden estimation compared with implantable cardiac monitors (ICMs). METHODS AND RESULTS: Using continuous ICM data from 346 patients in the CIRCA-DOSE trial, we simulated three smartwatch algorithms, assuming daytime wear (8:00 AM-10:00 p.m.). We also simulated commonly-used non-invasive intermittent rhythm monitoring strategies. Primary endpoints were sensitivity for arrhythmia recurrence and correlation with ICM-derived AF burden. Analyses were stratified by daily wear time and patient activity. AF recurrence occurred in 47.1% of patients. Simulated detection sensitivities were 82.2% (Apple Watch AF Burden), 70.6% (Apple Watch IRN), and 64.4% (Fitbit IHRD), compared with 15.8-64.6% for simulated intermittent AECG monitors. Wearables outperformed commonly-used Holter/patch monitoring strategies. AF burden correlation with ICM exceeded r = 0.97 for all algorithms. Among missed recurrences, median AF burden was <0.02%. Longer daily wear improved sensitivity (>90% with 24-hour use), whereas patient activity modestly reduced detection. CONCLUSION: Smartwatch-based AF detection algorithms demonstrate strong correlation with ICM-derived AF burden and clinically good sensitivity for recurrence detection, outperforming conventional non-invasive strategies. These findings support the integration of wearables as a scalable alternative for post-ablation rhythm monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".