Accuracy of smartphone apps for heart rate measurement

Background Smartphone manufacturers offer mobile health monitoring technology to their customers, including apps using the built-in camera for heart rate assessment. This study aimed to test the diagnostic accuracy of such heart rate measuring apps in clinical practice. Methods The feasibility and accuracy of measuring heart rate was tested on four commercially available apps using both iPhone 4 and iPhone 5. ‘Instant Heart Rate’ (IHR) and ‘Heart Fitness’ (HF) work with contact photoplethysmography (contact of fingertip to built-in camera), while ‘Whats My Heart Rate’ (WMH) and ‘Cardiio Version’ (CAR) work with non-contact photoplethysmography. The measurements were compared to electrocardiogram and pulse oximetry-derived heart rate. Results Heart rate measurement using app-based photoplethysmography was performed on 108 randomly selected patients. The electrocardiogram-derived heart rate correlated well with pulse oximetry (r = 0.92), IHR (r = 0.83) and HF (r = 0.96), but somewhat less with WMH (r = 0.62) and CAR (r = 0.60). The accuracy of app-measured heart rate as compared to electrocardiogram, reported as mean absolute error (in bpm ± standard error) was 2 ± 0.35 (pulse oximetry), 4.5 ± 1.1 (IHR), 2 ± 0.5 (HF), 7.1 ± 1.4 (WMH) and 8.1 ± 1.4 (CAR). Conclusions We found substantial performance differences between the four studied heart rate measuring apps. The two contact photoplethysmography-based apps had higher feasibility and better accuracy for heart rate measurement than the two non-contact photoplethysmography-based apps.

[1]  J. Silva,et al.  Use of smartphone technology in cardiology. , 2016, Trends in cardiovascular medicine.

[2]  L. O. Svaasand,et al.  Remote plethysmographic imaging using ambient light. , 2008, Optics express.

[3]  José Miguel Sotoca-Momblona,et al.  Does mHealth increase adherence to medication? Results of a systematic review , 2015, International journal of clinical practice.

[4]  J. O’Donovan,et al.  Controlling Ebola through mHealth strategies. , 2015, The Lancet. Global health.

[5]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[6]  Gaurav Arora,et al.  Tachycardia detection using smartphone applications in pediatric patients. , 2014, The Journal of pediatrics.

[7]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[8]  Amal Jubran,et al.  Pulse oximetry , 1999 .

[9]  Partho P Sengupta,et al.  Mobile technology and the digitization of healthcare. , 2016, European Heart Journal.

[10]  Y. Mendelson Pulse oximetry: theory and applications for noninvasive monitoring. , 1992, Clinical chemistry.

[11]  L. Bengtsson,et al.  Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti , 2011, PLoS medicine.

[12]  J. Chong,et al.  PULSE‐SMART: Pulse‐Based Arrhythmia Discrimination Using a Novel Smartphone Application , 2016, Journal of cardiovascular electrophysiology.

[13]  Ralph Maddison,et al.  The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: A systematic review , 2016, European journal of preventive cardiology.

[14]  Enrico Caiani,et al.  Acquisition and analysis of cardiovascular signals on smartphones: potential, pitfalls and perspectives , 2014, European journal of preventive cardiology.

[15]  G. Hartvigsen,et al.  Performance of the First Combined Smartwatch and Smartphone Diabetes Diary Application Study , 2015, Journal of diabetes science and technology.

[16]  Dermot Phelan,et al.  Accuracy of Wrist-Worn Heart Rate Monitors , 2017, JAMA cardiology.