BACKGROUND: The smartphone apps provide a user-friendly option for measurement of heart rate (HR) by detecting pulsatile photoplethysmographic signals with built-in cameras from the fingertips, however, the validation study is limited. METHODS: We compared HR detected by the smartphone apps (App1 = Instant HR, App2 = Cardiio: HR Monitor and App3 = Runtastic HR Monitor) with simultaneous standard ECG monitoring in the adult patients at the critical care unit. RESULTS: HR measurements were obtained from 140 patients with mean age 67.6 ± 15.3 years. Mean baseline HR was 89.1 ± 19.1 bpm (range, 32-136 bpm). Sinus rhythm was presented in 111 patients (79.3%), atrial fibrillation in 25 patients (17.9%), pacemaker rhythm in 3 patients (2.1%), and high-grade AV block in 1 patient (0.7%). The ECG-derived HR correlated well with App1 (r = 0.98), App2 (r = 0.97), and App3 (r = 0.92). In patients with regular rhythm, mean absolute deviation was 0.8 ± 1, 0.7 ± 0.9, 1.0 ± 1.3 bpm on App1, App2 and App3, respectively. In the patients with irregular rhythm, median absolute deviation (IQR) was 3 (2-5.5), 4 (1.5-11.5), and 6 (2-13) bpm. Skin colour did not affect with the HR measurement. CONCLUSIONS: HR measurements from all applications were correlated well with ECG monitoring. However, it was less accurate in case of irregular rhythm such as atrial fibrillation. Key messages Several reports on inaccuracy of mobile health apps have been published. We conducted the validation study in the real patients by using popular mobile apps. Heart rate measurements from mobile apps were correlated well with standard ECG. The accuracy of HR from apps was worse at irregular rate and tachycardia.
BACKGROUND: The smartphone apps provide a user-friendly option for measurement of heart rate (HR) by detecting pulsatile photoplethysmographic signals with built-in cameras from the fingertips, however, the validation study is limited. METHODS: We compared HR detected by the smartphone apps (App1 = Instant HR, App2 = Cardiio: HR Monitor and App3 = Runtastic HR Monitor) with simultaneous standard ECG monitoring in the adult patients at the critical care unit. RESULTS: HR measurements were obtained from 140 patients with mean age 67.6 ± 15.3 years. Mean baseline HR was 89.1 ± 19.1 bpm (range, 32-136 bpm). Sinus rhythm was presented in 111 patients (79.3%), atrial fibrillation in 25 patients (17.9%), pacemaker rhythm in 3 patients (2.1%), and high-grade AV block in 1 patient (0.7%). The ECG-derived HR correlated well with App1 (r = 0.98), App2 (r = 0.97), and App3 (r = 0.92). In patients with regular rhythm, mean absolute deviation was 0.8 ± 1, 0.7 ± 0.9, 1.0 ± 1.3 bpm on App1, App2 and App3, respectively. In the patients with irregular rhythm, median absolute deviation (IQR) was 3 (2-5.5), 4 (1.5-11.5), and 6 (2-13) bpm. Skin colour did not affect with the HR measurement. CONCLUSIONS: HR measurements from all applications were correlated well with ECG monitoring. However, it was less accurate in case of irregular rhythm such as atrial fibrillation. Key messages Several reports on inaccuracy of mobile health apps have been published. We conducted the validation study in the real patients by using popular mobile apps. Heart rate measurements from mobile apps were correlated well with standard ECG. The accuracy of HR from apps was worse at irregular rate and tachycardia.
Entities:
Keywords:
Electrocardiography; mobile health; photoplethysmography; pulse rate; skin colour
Authors: Ka Hou Christien Li; Francesca Anne White; Gary Tse; Timothy Tipoe; Tong Liu; Martin Cs Wong; Aaron Jesuthasan; Adrian Baranchuk; Bryan P Yan Journal: JMIR Mhealth Uhealth Date: 2019-02-15 Impact factor: 4.773