Journal of Medical Systems

, 40:199 | Cite as

A Systematic Review for Mobile Monitoring Solutions in M-Health

Mobile Systems
Part of the following topical collections:
  1. Advances in Ambient Intelligence for Health (AmIHEALTH 2015)

Abstract

A systematic review allows us to identify, assess, and interpret all possible relevant work associated with a question in particular or the subject of an area. Different authors can use several methodologies to learn about research related to their own research in different fields. The main objective of this review is to identify work, research and publications made in the field of the mobile monitoring of patients through some application or commercial or non-commercial solutions in m-Health. Next, we compare the different solutions with our solution, MoMo (Mobile Monitoring) Framework. MoMo is a solution that allows for patient mobile monitoring through mobile phones and biometric devices (blood pressure meter, glucometer and others). Our systematic review is based on the methodology of B. Kitchenham. She proposed specific guidelines for carrying out a systematic review in software engineering. We prepare our systematic review base in the selection of primary and secondary research related to mobile monitoring solutions following criteria with a specific weight to compare with each part of our research.

Keywords

Mobile monitoring Systematic review Ubiquitous computing M-health 

References

  1. 1.
    Kitchenham, B. (2004). Procedures for Performing Systematic Review.Google Scholar
  2. 2.
    BOSCH. (2011). Health Buddy System. Last access 2012, from http://www.bosch-telehealth.com.
  3. 3.
    AirStrip, T. (2011). AirStrip Patient Monitoring. Last access 2011, from http://www.airstriptech.com/.
  4. 4.
    WellDoc. (2011). WellDoc Health Platform. Last access 2012, from http://www.welldoc.com/Products-and-Services/Our-Products.aspx.
  5. 5.
    Georga, E., V. Protopappas, et al. (2009). Data mining for blood glucose prediction and knowledge discovery in diabetic patients: The METABO diabetes modeling and management system. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE.Google Scholar
  6. 6.
    Ryder, J., B. Longstaff, et al. (2009). Ambulation: A Tool for Monitoring Mobility Patterns over Time Using Mobile Phones. In Computational Science and Engineering, 2009. CSE ‘09. International Conference on.Google Scholar
  7. 7.
    Lorenz, A., D. Mielke, et al. (2007). Personalized mobile health monitoring for elderly. In 9th International Conference on Human Computer Interaction with Mobile Devices and Services, Singapore, ACM.Google Scholar
  8. 8.
    Wan-Young, C., L. Seung-Chul, et al. (2008). WSN based mobile u-healthcare system with ECG, blood pressure measurement function. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE.Google Scholar
  9. 9.
    Paradiso, R., A. Alonso, et al. (2008). Remote health monitoring with wearable non-invasive mobile system: The Healthwear project. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEGoogle Scholar
  10. 10.
    Mei, H., Widya, I., et al., A Flexible Vital Sign Representation Framework for Mobile Healthcare. In: In Pervasive Healthcare Conference and Workshops 2006. Innsbruck, Austria, 2006.Google Scholar
  11. 11.
    MobiHealth. (2003). MobiHealth Project. Last access 2011, from http://www. .org/.Google Scholar
  12. 12.
    Fernández-Luque, L., Sevillano, J.L., et al., eDiab: A System for Monitoring, Assisting and Educating People with Diabetes. In: In 10th International Conference, ICCHP 2006. Springer, Linz, Austria, 2006.Google Scholar
  13. 13.
    Tadj, C. and G. Ngantchaha (2006). Context handling in a pervasive computing system framework. In 3rd International Conference on Mobile Technology, Applications and Systems, Bangkok, Thailand, ACM.Google Scholar
  14. 14.
    Mamykina, L., Mynatt, E.D., et al., Investigating health management practices of individuals with diabetes. In: In SIGCHI conference on human factors in computing systems. ACM, Montreal, Quebec, 2006.Google Scholar
  15. 15.
    Villarreal, V., Hervas, R., Fontecha, F., and Bravo, J., Mobile monitoring framework to design parameterized and personalized m-health applications according to the patients diseases. Journal of Medical Systems. 39(132), 2015.Google Scholar
  16. 16.
    Villarreal V, Hervas R, Diez A, Bravo J. Applying Ontologies in the Develop- ment of Patient Mobile Monitoring Framework. In: 2nd.International Confer- ence on e-Health and Bioengineering (EHB). IEEE; 2009.Google Scholar
  17. 17.
    Fernandez-Lopez M. Overview of Methodology for Building Ontologies. In: Workshop on Ontologies and Problem-Solving Methods: Lessons Learned and Future Trends (IJCAI); 1999..Google Scholar
  18. 18.
    Villarreal V, Laguna J, Lopez S, Fontecha J, Fuentes C, Hervas R, et al. A Pro- posal for Mobile Diabetes Self-control: Towards a Patient Monitoring Frame- work. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing and Ambient Assisted Living (IWANN). vol. 5518. Springer; 2009. p. 870–877.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Vladimir Villarreal
    • 1
  • Ramón Hervás
    • 2
  • José Bravo
    • 2
  1. 1.GITCE Research LabTechnological University of PanamaPanamáPanamá
  2. 2.MAmI Research LabUniversity of Castilla-La ManchaCiudad RealSpain

Personalised recommendations