Mobile-Based Self-Monitoring for Preventing Patients with Type 2 Diabetes Mellitus and Hypertension from Cardiovascular Complication


Currently, there is less attention to the prevention of patients with type 2 diabetes mellitus and hypertension from cardiovascular complication, although it is the significant cause of death for these patients worldwide. To be prevented from this complication, these patients should develop their self-monitoring skills along with their healthcare journey. Consequently, this paper aims to provide an efficient mobile-based self-monitoring that can encourage patients with type 2 diabetes mellitus and hypertension to improve their health status. The distinctive point of the proposed mobile application is the trend progression module for demonstrating the progression of four health statuses for the patients. This trend progression is modeled with a fuzzy logic-based method. The rules are generated based on clinical data, lifestyle data, and experience of healthcare professionals. There are eleven healthcare professionals involved in this paper. The experiment with one hundred twenty-one patients shows that the proposed mobile application provides 92% trend progression accuracy compared with healthcare professionals’ decisions. The developed mobile application obtains the function satisfaction and performance satisfaction in the “strongly satisfied” level. Besides, the developed mobile application can encourage 85% of patients to improve their health statuses. It can be seen that this paper is a new aspect of encouraging patients to concern more about the improvement of their health statuses anywhere and anytime.

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  1. 1.

    World Health Organization. (2017). Noncommunicable diseases. Retrieved January, 2018, from

  2. 2.

    Mendis, S., Puska, P., & Norrving, B. (2011). Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization.

    Google Scholar 

  3. 3.

    Sowers, J. R., Epstein, M., & Frohlich, E. D. (2001). Diabetes, hypertension, and cardiovascular disease an update. Hypertension, 37(4), 1053–1059.

    Article  Google Scholar 

  4. 4.

    Campbell, N. R., Gilbert, R. E., Leiter, L. A., Larochelle, P., Tobe, S., Chockalingam, A., et al. (2011). Hypertension in people with type 2 diabetes update on pharmacologic management. Canadian Family Physician, 57(9), 997–1002.

    Google Scholar 

  5. 5.

    Osborne, R. H., Elsworth, G. R., & Whitfield, K. (2007). The health education impact questionnaire (heiQ): An outcomes and evaluation measure for patient education and self-management interventions for people with chronic conditions. Patient Education and Counseling, 66(2), 192–201.

    Article  Google Scholar 

  6. 6.

    Nikolic-Popovic, J., & Goubran, R. (2011). Measuring heart rate, breathing rate and skin conductance during exercise. In 2011 IEEE international symposium on medical measurements and applications (pp. 507–511). IEEE.

  7. 7.

    Puke, S., Suzuki, T., Nakayama, K., Tanaka, H., & Minami, S. (2013). Blood pressure estimation from pulse wave velocity measured on the chest. In 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 6107–6110). IEEE.

  8. 8.

    Luo, N., Ding, J., Zhao, N., Leung, B. H., & Poon, C. C. (2014). Mobile health: Design of flexible and stretchable electrophysiological sensors for wearable healthcare systems. In 11th international conference on wearable and implantable body sensor networks (pp. 87–91). IEEE.

  9. 9.

    Dexcom, I. (2017). Dexcom G4 platinum. Retrieved November, 2018, from

  10. 10.

    Dias, D., Ferreira, N., & Cunha, J. P. S. (2017). VitalLogger: An adaptable wearable physiology and body-area ambiance data logger for mobile applications. In 14th international conference on wearable and implantable body sensor networks (BSN) (pp. 71–74). IEEE.

  11. 11.

    Dias, D., & Paulo Silva Cunha, J. (2018). Wearable health devices—vital sign monitoring, systems and technologies. Sensors, 18(8), 2414.

    Article  Google Scholar 

  12. 12.

    Leijdekkers, P., & Gay, V. (2008). A self-test to detect a heart attack using a mobile phone and wearable sensors. In 21st IEEE international symposium on computer-based medical systems, 2008. CBMS’08 (pp. 93–98).

  13. 13.

    Mohan, P., Marin, D., Sultan, S., & Deen, A. (2008). MediNet: Personalizing the self-care process for patients with diabetes and cardiovascular disease using mobile telephony. In Engineering in medicine and biology society, 2008. EMBS 2008. 30th annual international conference of the IEEE (pp. 755–758). IEEE.

  14. 14.

    Instituto Carlos Slim de la Salud Voxiva. (2011). CardioNet. Retrieved January, 2018, from

  15. 15.

    Park, L. G., Howie-Esquivel, J., Chung, M. L., & Dracup, K. (2014). A text messaging intervention to promote medication adherence for patients with coronary heart disease: A randomized controlled trial. Patient Education and Counseling, 94(2), 261–268.

    Article  Google Scholar 

  16. 16.

    Park, M. J., Kim, H. S., & Kim, K. S. (2009). Cellular phone and internet-based individual intervention on blood pressure and obesity in obese patients with hypertension. International Journal of Medical Informatics, 78(10), 704–710.

    Article  Google Scholar 

  17. 17.

    Chow, C. K., Redfern, J., Thiagalingam, A., Jan, S., Whittaker, R., Hackett, M., et al. (2012). Design and rationale of the tobacco, exercise and diet messages (TEXT ME) trial of a text message-based intervention for ongoing prevention of cardiovascular disease in people with coronary disease: A randomised controlled trial protocol. British Medical Journal Open, 2, e000606.

    Google Scholar 

  18. 18.

    Sritara, P., Tatsanavivat, P., Tulyadachanon, S., Sangwatanaroj, S., Yamwong, S., & Vathesatogkit, P. (2015). To estimate cardio-vascular risk of Thai. Retrieved January, 2018, from

  19. 19.

    Rachata, N., & Temdee, P. (2016). Trend predictive model of cardiovascular complication for type 2 diabetes mellitus with hypertension patients. In Proceedings of global wireless summit (GWS) (pp. 219–223).

  20. 20.

    Kumar, A. V. (2014). Fuzzy expert systems for disease diagnosis. Hershey: IGI Global.

    Google Scholar 

  21. 21.

    Shang, K., & Hossen, Z. (2013). Applying fuzzy logic to risk assessment and decision-making (pp. 1–59). Ottawa: Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries.

    Google Scholar 

  22. 22.

    Adeli, A., & Neshat, M. (2010). A fuzzy expert system for heart disease diagnosis. In Proceedings of international multi conference of engineers and computer scientists, Hong Kong (pp. 134–139).

  23. 23.

    Ojokoh, B. A., Omisore, M. O., Samuel, O. W., & Ogunniyi, T. O. (2012). A fuzzy logic based personalized recommender system. International Journal of Computer Science and Information Technology and Security (IJCSITS), 2, 1008–1015.

    Google Scholar 

  24. 24.

    Kumar, S., & Jain, H. (2012). A fuzzy logic based model for life insurance underwriting when insurer is diabetic. European Journal of Applied Sciences, 4(5), 196–202.

    Google Scholar 

  25. 25.

    Kulkarni, G. H., & Waingankar, P. G. (2007). Fuzzy logic based traffic light controller. In International conference on industrial and information systems, 2007. ICIIS 2007 (pp. 107–110). IEEE.

  26. 26.

    Dudek, G., Strzelewicz, A., Krasowska, M., Rybak, A., & Turczyn, R. (2012). Fuzzy analysis of the cancer risk factor. Acta Physica Polonica B, 43(5), 947–960.

    Article  Google Scholar 

  27. 27.

    Alonso, A. L., Rosas-Jaimes, O. A., & Suárez-Cuenca, J. A. (2013). Fuzzy logic assisted diagnosis for atherogenesis risk. IFAC Proceedings Volumes, 46(31), 244–248.

    Article  Google Scholar 

  28. 28.

    Oad, K. K., DeZhi, X., & Butt, P. K. (2014). A fuzzy rule based approach to predict risk level of heart disease. Global Journal of Computer Science and Technology, 14(3-C), 17.

    Google Scholar 

  29. 29.

    Radha, P., & Srinvasan, B. (2014). Hybrid prediction model for the risk of cardiovascular disease in type-2 diabetic patients. International Journal, 2(10), 52–63.

    Google Scholar 

  30. 30.

    Kim, J., Lee, J., & Lee, Y. (2015). Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthcare Informatics Research, 21(3), 167–174.

    Article  Google Scholar 

  31. 31.

    Wilson, P. W., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998). Prediction of coronary heart disease using risk factor categories. Circulation, 97(18), 1837–1847.

    Article  Google Scholar 

  32. 32.

    Ross, T. J. (2009). Fuzzy logic with engineering applications. Hoboken: Wiley.

    Google Scholar 

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This research was sponsored by Mae Fah Luang University. The authors would like to acknowledge Dr. Worasak Rueangsirarak and Dr. Chayapol Kamyod, a lecturer at School of Information Technology, Mae Fah Luang University and Dr. Ekkapob Pianpises, MD., a family medicine physician, for the useful suggestions of this research.

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Correspondence to Punnarumol Temdee.

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Rachata, N., Temdee, P. Mobile-Based Self-Monitoring for Preventing Patients with Type 2 Diabetes Mellitus and Hypertension from Cardiovascular Complication. Wireless Pers Commun 117, 151–175 (2021).

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  • Cardiovascular complication
  • Mobile application
  • Prevention
  • Self-monitoring
  • Trend progression
  • Type 2 diabetes mellitus and hypertension