Skip to main content

Advertisement

Log in

H-PC: a cloud computing tool for supervising hypertensive patients

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Hypertension or high blood pressure is a condition on the rise. Not only does it affect the elderly but it is also increasingly spreading to younger sectors of the population. Treating it involves exhaustive monitoring of patients. Current health services can be improved to perform this task more effectively. A tool adapted to the particular requirements of hypertension can greatly facilitate monitoring and diagnosis. This paper presents the computer application Hypertension Patient Control (H-PC), which allows patients with hypertension to send their readings through mobile phone Short Message Service (SMS) or e-mail to a cloud computing datacenter. Through a graphic interface, clinicians can keep track of their patients, thus facilitating monitoring. Cloud-based datacenters provide a series of advantages in terms of scalability, maintainability, and massive data processing. However, the ability to guarantee Quality of Service (QoS) is crucial for the commercial success of cloud platforms. A novel and efficient cloud-based platform managing H-PC with QoS is also proposed in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Khazaei H, Misic J, Misic V (2012) Performance analysis of cloud computing centers using m/g/m/m+r queuing systems. IEEE Trans Parallel Distrib Syst 23(5):936–943

    Article  Google Scholar 

  2. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  3. Craig R, Mindell J (eds) (2006) Health survey for England 2006. Her Majesty’s Stationery Office, London

  4. NHS Information Centre. Quality and outcomes framework 2008/09. Online GP practice results database. http://www.qof.ic.nhs.uk/

  5. Law MR, Morris JK, Wald NJ (2009) Use of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies. BMJ 338:b1665

    Article  Google Scholar 

  6. Dickinson HO, Mason JM, Nicolson DJ et al (2006) Lifestyle interventions to reduce raised blood pressure: a systematic review of randomized controlled trials. J Hypertens 24:215–33

    Article  Google Scholar 

  7. Green BB, Cook AJ, Ralston JD et al (2008) Effectiveness of home blood pressure monitoring, web communication, and pharmacist care on hypertension control: a randomized controlled trial. JAMA. 299(24):2857–2867. doi:10.1001/jama.299.24.2857

    Article  Google Scholar 

  8. Pare G, Jaana M, Sicotte C (2007) Systematic review of home telemonitoring for chronic diseases: the evidence base. J Am Med Inform Assoc 14:269–77

    Article  Google Scholar 

  9. Pickering TG, Miller NH, Ogedegbe G et al (2008) Call to action on use and reimbursement for home blood pressure monitoring: a joint scientific statement from the American Heart Association, American Society of Hypertension, and Preventive Cardiovascular Nurses Association. Hypertens 52:10–29

    Article  Google Scholar 

  10. Ohkubo T, Imai Y, Tsuji I et al (1998) Home blood pressure measurement has a stronger predictive power for mortality than does screening blood pressure measurement: a population-based observation in Ohasama. Jpn J Hypertens 16:971–975

    Article  Google Scholar 

  11. Bobrie G, Chatellier G, Genes N et al (2004) Cardiovascular prognosis of masked hypertension detected by blood pressure self-measurement in elderly treated hypertensive patients. JAMA 291:1342–1349

    Article  Google Scholar 

  12. Aversa R, Di Martino B, Rak M, Venticinque S, Villano U (2011) Performance prediction for HPC on clouds. Principles and paradigms, Cloud Computing

    Google Scholar 

  13. Vishwanath KV, Nagappan N (2010) Characterizing cloud computing hardware reliability. In: Proceedings of the 1st ACM symposium on cloud computing (SoCC ’10), 193–204 2010

  14. Iosup A,Yigitbasi N, Epema D (2011) On the performance Variability of Production cloud services. 11th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid’2011), 104–113, 2011

  15. Martinello M, Kaniche M, Kanoun K (2005) Web service availability: impact of error recovery and traffic model. J Reliab Eng Syst Saf 89(1):6–16

    Article  Google Scholar 

  16. Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420

    Article  Google Scholar 

  17. Amazon Elastic Compute Cloud (EC2). Available at: http://www.amazon.com/ec2/. 2013

  18. Chappell D Introducing the Azure services platform. White Paper, October 2008.

  19. Google App Engine. Available at: http://appengine.google.com. 2013

  20. Calheiros R, Ranjan R, Beloglazov A, De Rose C, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Soft Pract Exp 41(1):23–50

    Article  Google Scholar 

  21. Vilaplana F, Abella F, Filgueira R, Rius J (2013) The cloud paradigm applied to e-health. BMC Med Inf Decis Mak 13(1):35

    Article  Google Scholar 

  22. Kliazovich D, Bouvry P, Khan S (2010) GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput

  23. Lim S, Sharma B, Nam G, Kim E, Das c (2009) MDCSIM: a multi-tier data center simulation platform. In: Proceedings of IEEE international conference on cluster computing, 2009

  24. Abbadi IM, Namiluko C, Martin A (2011) Insiders analysis in Cloud computing focusing on home healthcare system. In: 2011 international conference for internet technology and secured transactions, 350,357, 11–14 Dec. 2011.

  25. Deng M, Petkovic M, Nalin M, Baroni I (2011) A home healthcare system in the cloud-addressing security and privacy challenges. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), vol., no., pp. 549,556, 4–9 July 2011 doi:10.1109/CLOUD.2011.108.

  26. McManus RJ et al (2010) Telemonitoring and self-management in the control of hypertension (TASMINH2): a randomised controlled trial. Lancet 376(9736):163–172

    Article  Google Scholar 

  27. Bray EP, Holder R , Mant J , McManus RJ (2010) Does self monitoring reduce blood pressure? analysis with metaregression of randomized controlled trials. Ann Med 42(5):371–386

  28. Ogedegbe G, Schoenthaler A (2006) A systematic review of the eff ects of home blood pressure monitoring on medication adherence. J Clin Hypertens (Greenwich) 8:174–80

    Article  Google Scholar 

  29. Kroenke K et al (2010) Effect of telecare management on pain and depression in patients with cancer: A randomized trial. JAMA 304(2):163–171

    Article  Google Scholar 

  30. Patel B, Turban S, Anderson C, Charleston J, Miller E, Appel L (2010) A comparison of web sites used to manage and present home blood pressure readings. J Clin Hypertens 12(6):389–395

    Article  Google Scholar 

  31. Karwowski W, Soares MM, Stanton NA (2011) Human factors and ergonomics in consumer product design: methods and techniques (Handbook of human factors in consumer product design): needs analysis: or, how do you capture, represent, and validate user requirements in a formal manner/notation before design?. CRC Press, Florida Chapter 26 by K Tara Smith

  32. Nielsen J, Landauer T. A mathematical model of the finding of usability problems. In: Proceedings of ACM INTERCHI’93 Conference Amsterdam, ACM Press, Amsterdam, Netherlands, pp. 206–213 1993.

  33. Dubrova E (2013) Fault-tolerant design. Springer, ISBN 978-1-4614-2112-2 2013

  34. Apache JMeter. webpage http://jmeter.apache.org/

  35. Len A et al (2011) A new multidisciplinary home care telemedicine system to monitor stable chronic human immunodeficiency virus-infected patients: a randomized study. PLoS ONE 6(1):e14515

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the MEyC under contract TIN2011-28689-C02-02. Some of the authors are members of the research group 2009 SGR145, funded by the Generalitat de Catalunya.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francesc Solsona.

Additional information

This work was supported by the MEyC under contracts TIN2011-28689-C02-02. The authors are members of the research group 2009-SGR145, funded by the Generalitat de Catalunya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vilaplana, J., Solsona, F., Abella, F. et al. H-PC: a cloud computing tool for supervising hypertensive patients. J Supercomput 71, 591–612 (2015). https://doi.org/10.1007/s11227-014-1312-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1312-9

Keywords

Navigation