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Primary Prevention of Asymptomatic Cardiovascular Disease Using Physiological Sensors Connected to an iOS App

  • Mobile & Wireless Health
  • Published:
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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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References

  1. World Health Organization In: Mensah, G., Mackay, J., and Mensah, G. (Eds.), The atlas of heart disease and stroke. World Health Organization, Geneva, 2004.

    Google Scholar 

  2. Banegas, J.R., Rodríguez-Artalejo, F., Graciani, A., Villar, F., and Herruzo, R., Mortality attributable Yap J, Noh YH, Jeong DU. The deployment of novel techniques for mobile ECG monitoring. International Journal of Smart Home. 6(4):1–14, 2012.

    Google Scholar 

  3. Martínez-Pérez, B., de la Torre-Díez, I., López-Coronado, M., and Herreros-González, J., Mobile apps in cardiology: Review. JMIR Mhealth Uhealth. 1(2):e15, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Fuster, V., Epidemic of cardiovascular disease and stroke: The three main challenges. Circulation. 99:1132–1137, 1999.

    Article  CAS  PubMed  Google Scholar 

  5. Seto, E., Leonard, K.J., Cafazzo, J.A., Barnsley, J., Masino, C., and Ross, H.J., Developing healthcare rule-based expert systems: Case study of a heart failure telemonitoring system. Int J Med Inform. 81(8):556–565, 2012.

    Article  PubMed  Google Scholar 

  6. Fuster, V., and Pearson, T.A., 27th Bethesda conference (task force 8). Matching the intensity of risk factor management with the hazard for coronary disease events. J Am Coll Cardiol. 27:1039, 1996.

    Article  Google Scholar 

  7. Banegas, J.R., Epidemiología de la hipertensión arterial en España. Situación actual y perspectivas. Hipertensión. 22:353–362, 2005.

    Article  Google Scholar 

  8. Coca Payeras, A., Evolución del control de la hipertensión arterial en atención Primaria en España. Resultados del estudio Controlpres 2003. Hipertensión. 22:5–14, 2005.

    Article  Google Scholar 

  9. Jayachandran, E.S., et al., Analysis of myocardial infarction using discrete wavelet transform. J Med Syst. 34(6):985–992, 2010.

    Article  CAS  PubMed  Google Scholar 

  10. Kilsdonk, E., Peute, L.W., Riezebos, R.J., Kremer, L.C., and Jaspers, M., From an expert-driven paper guideline to a user-centred decision support system: A usability comparison study. Artif Intell Med. 59(1):5–13, 2013.

    Article  PubMed  Google Scholar 

  11. Kahai, P., Namuduri, K.R., and Thompson, H., Decision support for automated screening of diabetic retinopathy. In Signals, Systems and Computers. 2:1630–1634, 2004.

    Google Scholar 

  12. Schuh, C. J., de Bruin, J. S., Seeling, W., Acceptability and difficulties of (fuzzy) decision support systems in clinical practice. In IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) 257–262, 2013.

  13. Leijdekkers, P., and Gay, V., A self-test to detect a heart attack using a mobile phone and wearable sensors. Conf Proc IEEE CBMS:93–98, 2008.

  14. Upatising, B., Wood, D.L., Kremers, W.K., Christ, S.L., Yih, Y., Hanson, G.J., and Takahashi, P.Y., Cost comparison between home Telemonitoring and usual Care of Older Adults: A randomized trial (Tele-ERA). Telemedicine and e- Health:3–8, 2015.

  15. Nakamura, H., Shimada, K., Fujie, T., A comparative evaluation between condition of the wrist band capacitively-coupled ECG recording through signal-to-noise ratio. IEEE EMBS 29th Annual International Conference, 23–26 August 1–4244–0788-5/07, 5886–5889, 2007.

  16. World Health Organization. Disease and injury regional estimates, cause-specific mortality: regional estimates for 2012. 2012. Available from: http://www.who.int/mediacentre/factsheets/fs310/es/ (last accessed 17 Sept 2017).

  17. Google. Google Play. 2016. Available from: http://play.google.com/store (last accessed 22 Sept 2017).

  18. Adhikari, R., Richards, D., Scott, K., Security and privacy issues related to the use of mobile health apps. 25th Australasian Conference on Information Systems, 8-10 December, Auckland, New Zealand, 2014.

  19. McLellan, S., Muddimer, A., and Peres, S.C., The effect of experience on system usability scale ratings. J Usability Stud. 7(2):56–67, 2012.

    Google Scholar 

  20. Mahmood, S.S., Levy, D., Vasan, R.S., and Wang, T.J., The Framingham heart study and the epidemiology of cardiovascular disease: A historical perspective. Lancet. 383(9921):999–1008, 2014.

    Article  PubMed  Google Scholar 

  21. Ackerman, R., Parush, A., Nassar, F., and Shtub, A., Metacognition and system usability: Incorporating metacognitive research paradigm into usability testing. Comput Hum Behav. 54:101–113, 2016.

    Article  Google Scholar 

  22. Athilingam, P., Labrador, M.A., Remo, E.F.J., Mack, L., and Elliott, A.F., Features and usability assessment of a patient-centered mobile application (HeartMapp) for self-management of heart failure. Appl Nurs Res. 32:156–163, 2016.

    Article  PubMed  Google Scholar 

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Acknowledgements

This research has been partially supported by the project “Elkartek eBihotza” and the eVida Group of Basque Government.

The authors would especially like to express their appreciation to the University of Deusto/DeustoTech, Cruces Hospital and the heart disease association for all their support in specifying, conducting and evaluating the research.

Funding

This study was funded by the project “Elkartek eBihotza” of Basque Government, Spain.

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Correspondence to Isabel de la Torre Díez.

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The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the Ethic Committee of the “Universidad de Deusto” (Ref. ETK-19/16–17).

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This article is part of the Topical Collection on Mobile & Wireless Health

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Moreno-Alsasua, L., Garcia-Zapirain, B., David Rodrigo-Carbonero, J. et al. Primary Prevention of Asymptomatic Cardiovascular Disease Using Physiological Sensors Connected to an iOS App. J Med Syst 41, 191 (2017). https://doi.org/10.1007/s10916-017-0840-2

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  • DOI: https://doi.org/10.1007/s10916-017-0840-2

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