JMIR Publications

ABSTRACT

Background:

Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, non-invasive monitoring.

Objective:

We aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch.

Methods:

Forty adults presenting to a cardiology clinic wore a smartwatch and performed scripted movements to simulate activities of daily living (ADLs). Clinical and sociodemographic characteristics of participants were abstracted from medical records. Participants completed a questionnaire assessing usability and different domains of their experience with the device. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared to gold-standard Holter monitoring.

Results:

Average age of participants was 71  8 years, most had AF risk factors, and 9 (23%) were in AF. About half of participants owned smartphones but none owned smartwatches. Participants wore the smartwatch for 42  14 minutes while generating motion noise to simulate ADLs. The algorithm determined 53 of 314 30-second noise-free pulse segments as consistent with AF. Compared to the gold-standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively.

Conclusions:

A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants simulating ADLs. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.