Accuracy of real time heart rate monitoring using smartwatch in a stroke care unit

FUNDING ACKNOWLEDGEMENTS: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): ISCIII Smartwatch with heart rate (HR) tracker measures may allow for identification of people with high resting HR who have a higher risk of atrial fibrillation (AF), a potent independent risk factor for embolic strokes. In this research, we propose a new protocol for collecting real time data of patients with cryptogenic stroke including HR at rest along with monitoring electrocardiogram (ECG). First, we assess the concordance of HR measurement between telemetry (TLM) and two smartwatches in acute stroke patients, as well as the determinants of measurement error. METHOD: We included patients over 50 years of age hospitalized in our Stroke Unit who were monitored by TLM and used the Garmin Vivosmart HR® (S1) and Fitbit Charge 5® (S2) devices. Mean HR was collected at 5, 10, 15, 20, and 25 minutes. We applied the Bland-Altman method in combination with Lin's Concordance Correlation Coefficient (CCC) to assess agreement between measurements. A coefficient CCC > 0.9 indicates acceptable agreement. RESULTS: The experimental setup was performed using 49 stroke patients (59% women, mean age 78 ± 9.86 years, and 69% with a history of AF). Poor agreement was observed between HR measurements by SI or S2 and TLM (CCC = 0.48 and 0.57 respectively), which was conditioned by the presence of AF during the measurement (CCC = -0.17 and -0.06). There was only good concordance in the absence of AF (CCC = 0.97 and 0.92). A higher percentage of error between measurements was significantly associated with a HR > 90 bpm, a history of AF, or presenting AF during the measurement (p<0.001). CONCLUSIONS: Real-time monitoring of HR at rest in each smartwatch was not accurate in acute stroke patients who had presented AF during the intake, or when HR by TLM was greater than 90. This suggests certain limitations when considering the application of processed data from this wearable technology.