iPhone App compared with standard blood pressure measurement -The iPARR trial.

BACKGROUND: The possibility to use built-in smartphone-cameras for photoplethysmographic (PPG) recording of pulse waves lead to the release of numerous health apps, claiming to measure blood pressure (BP) based on PPG signals. Even though these apps are highly popular, not a single one is clinically validated. Aim of the current study was to test systolic BP (sBP) estimation by a promising new algorithm in a large clinical setting. METHODS: The study was designed based on the European Society of Hypertension International Protocol Revision 2010. Each individual received 7 sequential BP measurements, starting with the reference device - an automated oscillometric cuff device - followed by the PPG recording at the patients' index finger. RESULTS: A total 1,036 subjects were recruited of which 965 could be included for final analysis leading to 2,895 pairs of comparison. Mean (±SD) error between test and reference device was -0.41 (±16.52) mmHg. Only 38.1% of all 2,895 BP comparisons reached a delta within ±5 mmHg, while 29.3% reached a delta larger than 15 mmHg. Bland-Altman plot showed an overestimation of smartphone sBP in comparison to reference sBP in low range and an underestimation in high sBP range. CONCLUSIONS: According to the European Society of Hypertension International Protocol Revision 2010 specifications the algorithm failed validation criteria for sBP measurement and was not commercialized. These findings emphasize that health apps should be rigorously validated according to common guidelines before market release as under- and/or overestimation of BP is potentially exposing persons at health risks in short and long term. TRIAL REGISTRATION: ClinicalTrials.gov, number NCT02552030.

The WATCH AF Trial: SmartWATCHes for Detection of Atrial Fibrillation.

OBJECTIVES: The WATCH AF (SmartWATCHes for Detection of Atrial Fibrillation) trial compared the diagnostic accuracy to detect atrial fibrillation (AF) by a smartwatch-based algorithm using photoplethysmographic (PPG) signals with cardiologists' diagnosis by electrocardiography (ECG). BACKGROUND: Timely detection of AF is crucial for stroke prevention. METHODS: In this prospective, 2-center, case-control trial, a PPG pulse wave recording using a commercially available smartwatch was obtained along with Internet-enabled mobile ECG in 672 hospitalized subjects. PPG recordings were analyzed by a novel automated algorithm. Cardiologists' diagnoses were available for 650 subjects, although 142 (21.8%) datasets were not suitable for PPG analysis, among them 101 (15.1%) that were also not interpretable by the automated Internet-enabled mobile ECG algorithm, resulting in a sample size of 508 subjects (mean age 76.4 years, 225 women, 237 with AF) for the main analyses. RESULTS: For the PPG algorithm, we found a sensitivity of 93.7% (95% confidence interval [CI]: 89.8% to 96.4%), a specificity of 98.2% (95% CI: 95.8% to 99.4%), and 96.1% accuracy (95% CI: 94.0% to 97.5%) to detect AF. CONCLUSIONS: The results of the WATCH AF trial suggest that detection of AF using a commercially available smartwatch is in principle feasible, with very high diagnostic accuracy. Applicability of the tested algorithm is currently limited by a high dropout rate as a result of insufficient signal quality. Thus, achieving sufficient signal quality remains challenging, but real-time signal quality checks are expected to improve signal quality. Whether smartwatches may be useful complementary tools for convenient long-term AF screening in selected at-risk patients must be evaluated in larger population-based samples. (SmartWATCHes for Detection of Atrial Fibrillation [WATCH AF]:; NCT02956343).

Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO).

AIMS: Early detection of atrial fibrillation (AF) is essential for stroke prevention. Emerging technologies such as smartphone cameras using photoplethysmography (PPG) and mobile, internet-enabled electrocardiography (iECG) are effective for AF screening. This study compared a PPG-based algorithm against a cardiologist's iECG diagnosis to distinguish between AF and sinus rhythm (SR). METHODS AND RESULTS: In this prospective, two-centre, international, clinical validation study, we recruited in-house patients with presumed AF and matched controls in SR at two university hospitals in Switzerland and Germany. In each patient, a PPG recording on the index fingertip using a regular smartphone camera followed by iECG was obtained. Photoplethysmography recordings were analysed using an automated algorithm and compared with the blinded cardiologist's iECG diagnosis. Of 672 patients recruited, 80 were excluded mainly due to insufficient PPG/iECG quality, leaving 592 patients (SR: n = 344, AF: n = 248). Based on 5 min of PPG heart rhythm analysis, the algorithm detected AF with a sensitivity of 91.5% (95% confidence interval 85.9-95.4) and specificity of 99.6% (97.8-100). By reducing analysis time to 1 min, sensitivity was reduced to 89.9% (85.5-93.4) and specificity to 99.1% (97.5-99.8). Correctly classified rate was 88.8% for 1-min PPG analysis and dropped to 60.9% when the threshold for the analysed file was set to 5 min of good signal quality. CONCLUSION: This is the first prospective clinical two-centre study to demonstrate that detection of AF by using a smartphone camera alone is feasible, with high specificity and sensitivity. Photoplethysmography signal analysis appears to be suitable for extended AF screening. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, number NCT02949180, https://clinicaltrials.gov/ct2/show/NCT02949180.

Performance of a Blood Pressure Smartphone App in Pregnant Women: The iPARR Trial (iPhone App Compared With Standard RR Measurement).

Hypertensive disorders are one of the leading causes of maternal death worldwide. Several smartphone apps claim to measure blood pressure (BP) using photoplethysmographic signals recorded by smartphone cameras. However, no single app has been validated for this use to date. We aimed to validate a new, promising smartphone algorithm. In this subgroup analysis of the iPARR trial (iPhone App Compared With Standard RR Measurement), we tested the Preventicus BP smartphone algorithm on 32 pregnant women. The trial was conducted based on the European Society of Hypertension International Protocol revision 2010 for validation of BP measuring devices in adults. Each individual received 7 sequential BP measurements starting with the reference device (Omron-HBP-1300) and followed by the smartphone measurement, resulting in 96 BP comparisons. Validation requirements of the European Society of Hypertension International Protocol revision 2010 were not fulfilled. Mean (±SD) systolic BP disagreement between the test and reference devices was 5.0 (±14.5) mm Hg. The number of absolute differences between test and reference device within 5, 10, and 15 mm Hg was 31, 53, and 64 of 96, respectively. A Bland-Altman plot showed an overestimation of smartphone-determined systolic BP in comparison with reference systolic BP in low range but an underestimation in medium-range BP. The Preventicus BP smartphone algorithm failed the accuracy criteria for estimating BP in pregnant women and was thus not commercialized. Pregnant women should be discouraged from using BP smartphone apps, unless there are algorithms specifically validated according to common protocols. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT02552030.