Blood pressure measurement and mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management. By maximizing the smartphone’s optics and computational power, they could allow the assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP).
Summarize of Blood pressure measurement
The parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter.
Compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP) measurements.
Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%).
The difference in BP (mean ± standard deviation) between both methods were within the ISO 81,060–2:2018 standard for SBP (− 0.7 ± 7.7 mmHg), DBP (− 0.4 ± 4.5 mmHg) and MBP (− 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app.
This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.
High blood pressure (BP) remains the leading risk factor for death and disability in both high and low-income countries. Its complications are responsible for the deaths of approximately ten million people annually, a 50% increase over the estimates from 1990. By 2025, the number of people suffering from hypertension will reach 1.5 billion. The impact of this disease represents a daunting burden to any healthcare system.
Digital health approaches, and in particular mobile health (mHealth) diagnostics, have been shown to be effective, scalable, and sustainable for chronic disease prevention and management. Mobile phones represent a widespread, readily available device for mHealth. Worldwide, over one-third of consumers own a mobile phone12,13.
If the accuracy of reliable smartphone-based blood pressure measurements was to be demonstrated, this would be a promising tool that could improve access to more populations, medical record-keeping, analysis of blood pressure measurements for hypertension management as well as medication compliance and health education.
Interest in “cuffless” BP measurement, using smartphones or wearable sensor technologies that estimate BP from photoplethysmograms (PPG) is rapidly increasing14. In particular, pulse wave analysis techniques15, which derive central blood pressure from the morphology of pulse waves in peripheral tissues, are a growing area of interest16. Recently, the first wrist-worn device using Pulse Wave Transit Time (PWTT) has obtained a 510(k) clearance by the US Food and Drug Administration for tracking changes in BP following a calibration process using an oscillometric BP monitor (https://www.accessdata.fda.gov/cdrh_docs/pdf19/K190792.pdf, accessed December 10th 2019). However, none of the smartphone-based applications has yet been clinically validated or demonstrated to be accurate.
Methodology for training OptiBP
Anesthesia protocol and signal acquisition
On the day of surgery, patients were monitored according to the standards of our departments and connected to a Philips IntelliVue MP50 monitor (Philips, Amsterdam, the Netherlands). A 20-gauge, 4.5 cm length arterial catheter (BD FlowswitchTM, Becton–Dickinson, Franklin Lakes, USA) was inserted under local anaesthesia predominantly into the left or right radial artery. The transducer for the intra-arterial catheter was kept at the level of the left ventricle of the heart. A flush test was performed to rule out over- or under-damping22.