Background: There have been limited data examining the temporal relationship between device-derived daily activity and ventricular arrhythmias (VAs).
Objective: We aimed to assess whether activity predicted VAs or VAs predicted changes in activity.
Methods: The CERTITUDE registry includes over 55,000 implanted devices active on Home Monitoring.
Background: Despite clear associations between arrhythmia burden and cardiovascular risk, clinical risk scores that predict cardiovascular events do not incorporate individual-level arrhythmia characteristics from long-term continuous monitoring (LTCM).
Objectives: This study evaluated the performance of risk models that use data from LTCM and patient claims for prediction of heart failure (HF) and ischemic stroke.
Methods: We retrospectively analyzed features extracted from up to 14 days of LTCM electrocardiogram (ECG) data linked to patient-level claims data for 320,974 Medicare beneficiaries who underwent ZioXT ambulatory monitoring.
The rapid growth in consumer-facing mobile and sensor technologies has created tremendous opportunities for patient-driven personalized health management. The diagnosis and management of cardiac arrhythmias are particularly well suited to benefit from these easily accessible consumer health technologies. In particular, smartphone-based and wrist-worn wearable electrocardiogram (ECG) and photoplethysmography (PPG) technology can facilitate relatively inexpensive, long-term rhythm monitoring.
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