Advanced and Accurate Mobile Health Tracking Devices Record New Cardiac Vital Signs

Abstract

Cardiovascular disease remains the leading cause of death and disease worldwide. As demands on an already resource-constrained healthcare system intensify, disease prevention in the future will likely depend on out-of-office monitoring of cardiovascular risk factors. Mobile health tracking devices that can track blood pressure and heart rate, in addition to new cardiac vital signs, such as physical activity level and pulse wave velocity (PWV), offer a promising solution. An initial barrier is the development of accurate and easily-scalable platforms. In this study, we made a customized smartphone app and used mobile health devices to track PWV, blood pressure, heart rate, physical activity, sleep duration, and multiple lifestyle risk factors in ≈250 adults for 17 continual weeks. Eligible participants were identified by a company database and then were consented and enrolled using only a smartphone app, without any special training given. Study participants reported high overall satisfaction, and 73% of participants were able to measure blood pressure and PWV, <1 hour apart, for at least 14 of 17 weeks. The study population's blood pressure, PWV, heart rate, activity levels, sleep duration, and the interrelationships among these measurements were found to closely match either population averages or values obtained from studies performed in a controlled setting. As a proof-of-concept, we demonstrated the accuracy and ease, as well as many challenges, of using mHealth technology to accurately track PWV and new cardiovascular vital signs at home.

Keywords: blood pressure; cardiovascular disease; hypertension; smartphone; vital signs.

Figure 1. Screenshots of mobile app developed for the study

A) Screenshot of invitation sent to eligible participants for study enrollment B) Diagram used to explain the study protocol, and to obtain informed consents C) Weekly dashboard presented to app users detailing progress and successful completion of study measurements.

Figure 2. Histogram Plot of Adherence Data

Represented is a histogram of the number of participants that successfully measured blood pressure and pulse wave velocity, less than 1 hour apart according to number of weeks in the study. For example, 49 individuals signed up for the study online, but never recorded a measurement. This is represented by the 1^st column, or 0 number of weeks.

Figure 3. Correlation of pulse wave velocity with mean arterial pressure (MAP) and age

Pulse wave velocity demonstrated the strongest relationship with A) MAP (R²=0.13) and B) age (R²=0.20) in the population not taking anti-hypertensives.

Figure 4. Prediction of hypertension using PWV and BMI

A) A logistic regression model using only PWV, standard PWV and BMI could predict hypertension with an area under the curve (AUC) of 0.81 (sensitivity = 0.89, specificity = 0.63).