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Primary Prevention of Asymptomatic Cardiovascular Disease Using Physiological Sensors Connected to an iOS App
Leire Moreno-Alsasua1, Begonya Garcia-Zapirain1, J David Rodrigo-Carbonero2
1eVIDA - DeustoTechLIFE Research Group, Universidad de Deusto, Avda/Universidades 24, 48007, Bilbao, Spain.
Insights
A new technology solution uses sensors to monitor cardiovascular disease risk factors in asymptomatic individuals. This system promotes heart health awareness through self-feedback, aiding in early detection and prevention strategies.
Area of Science:
- Biomedical Engineering
- Cardiovascular Health Technology
- Preventive Medicine
Background:
- Cardiovascular disease (CVD) is a leading cause of death and disability globally.
- Increasing CVD prevalence necessitates innovative primary prevention strategies for asymptomatic individuals.
- Current prevention methods lack integrated, real-time monitoring of key risk factors.
Purpose of the Study:
- To design and develop a technology solution for the primary prevention of cardiovascular disease.
- To enhance public awareness of healthy heart habits using self-feedback mechanisms.
- To enable early detection of cardiovascular risk factors in asymptomatic patients.
Main Methods:
- Development of a modular system comprising an iOS app, sensors, server, and web interface.
- Utilized Bluetooth 4.0 (CoreBluetooth) for sensor-app connectivity.
- Integrated sensors to measure heart rate, blood pressure, SpO2 (oxygen saturation), and body temperature.
- Employed CoreData and SQL for data storage on iPad and server.
- Validated the system with 20 healthy volunteers and 10 patients with structural heart disease.
Main Results:
- Identified 32 statistically significant correlations (p < 0.01) between physiological variables and risk factors.
- Demonstrated an inverse relationship between daily step count and high blood pressure (p = 0.008).
- Found significant correlations between cardiovascular risk and age (p = 0.013) among other findings (24 cases, p < 0.05).
Conclusions:
- The developed system effectively collects data on cardiovascular risk factors via physiological sensors and activity tracking.
- The technology facilitates primary prevention by increasing awareness and enabling self-monitoring of heart health.
- This solution offers a promising approach for proactive cardiovascular disease management in the general population.
Abstract:
Cardiovascular disease is the first cause of death and disease and one of the leading causes of disability in developed countries. The prevalence of this disease is expected to increase in coming years although the death rate may be lower due to better treatment. To present the design and development of a technology solution for primary prevention of cardiovascular disease in asymptomatic patients. The system aims to raise the population's awareness of the importance of adopting healthy heart habits by using self-feedback techniques. A series of sensors which makes it possible to detect cardiovascular risk factors in asymptomatic patients were used. These sensors enable evaluation of heart rate, blood pressure, SpO2 -oxygen saturation in blood- and body temperature. This work has developed a modular solution centred on four parts: iOS app, sensors, server and web. The CoreBluetooth library, which carries out Bluetooth 4.0 communication, was used for the connection between the app and the sensors. The data files are stored on the iPad and the server by using CoreData and SQL mechanisms. The system was validated with 20 healthy volunteers and 10 patients with established structural heart disease. Once the samples had been obtained, a comparison of all the significant data was run, in addition to a statistical analysis. The result of this calculation was a total of 32 cases of first level significance correlations (p < 0.01), for example, the inverse relationship between the daily step count and high blood pressure (p = 0.008) and 24 s level cases (p < 0.05) such as the significant correlation between risk and age (p = 0.013). The system designed in this paper has made it possible to create an application capable of collecting data on cardiovascular risk factors through a sensor system that measures physiological variables and records physical activity and diet.
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