JMIR Publications

ABSTRACT

Background:

Stroke, as a leading cause of death around the globe, has become a heavy burden on our society. Studies show that stroke can be predicted and prevented if a person’s blood pressure (BP) status is appropriately monitored via an ABPM (Ambulatory Blood Pressure Monitor) system. However, currently, there exists no efficient and user-friendly ABPM system to provide stroke-risk early warning in real-time. Moreover, most existing ABPM devices utilized measure Blood Pressure (BP) during the deflation of the cuff, which fails to reflect blood pressure accurately.

Objective:

In this study, we seek to develop a new ABPM Mobile Health (mHealth) system that is capable of monitoring blood pressure during inflation and could detect the early stroke risk signals in real-time.

Methods:

We design an ABMP mHealth system that is based on mobile networks infrastructure and mobile apps. The proposed system contains two major parts: a new ABPM device in which inflation-type BP measurement algorithm is embedded, and an abnormal BP data analyzing (ABA) algorithm for stroke risk prediction service at our health data service center. For evaluation, the ABPM device was first tested using simulated signals and compared with the “Gold Standard” of a mercury sphygmomanometer. Then, the performance of our proposed mHealth system was evaluated in an observational study.

Results:

The results are presented in two main parts, the device test and the longitudinal observational studies of the presented system. The average measurement error of the new ABPM device with the inflation-type algorithm was less than 0.55 mmHg compared to a reference device with using simulated signals. Moreover, the results of correlation coefficients and agreement analysis show that there is a strong linear correlation between our device and the standard mercury sphygmomanometer. In the case of the system observational study, we collected a data set with 88 features, including real-time data as well as user information and records. Our abnormal BP data analyzing (ABA) algorithm achieved the best performance for three risk levels, and for the most concerned high-risk level, the accuracy and AUC is 0.885 and 0.912, respectively. Our system enables a patient to be aware of his/her risk in real-time, which improves medication adherence with risk self-management.

Conclusions:

To our best knowledge, this device is the first ABPM device that measures blood pressure during the inflation process and has obtained a government medical license. Device tests and longitudinal observational studies were conducted in PKU (Peking University) hospitals, showing the high accuracy of BP measurements, and its efficiency in detecting early signs of stroke and providing stroke-risk early warning.