Appl Clin Inform 2016; 07(04): 1120-1134
DOI: 10.4338/ACI-2015-12-RA-0172
Research Article

BPcontrol

A Mobile App to Monitor Hypertensive Patients
Adrián Carrera
1   Department of Computer Science and INSPIRES. University of Lleida, C/ Jaume II 69, E-25001 Lleida, Spain
,
Marc Pifarré
1   Department of Computer Science and INSPIRES. University of Lleida, C/ Jaume II 69, E-25001 Lleida, Spain
,
Jordi Vilaplana
1   Department of Computer Science and INSPIRES. University of Lleida, C/ Jaume II 69, E-25001 Lleida, Spain
,
Josep Cuadrado
2   Hesoft Group, Partida Bovà 15, 25196 LLeida, Spain
,
Sara Solsona
2   Hesoft Group, Partida Bovà 15, 25196 LLeida, Spain
,
Jordi Mateo
1   Department of Computer Science and INSPIRES. University of Lleida, C/ Jaume II 69, E-25001 Lleida, Spain
,
Francesc Solsona
1   Department of Computer Science and INSPIRES. University of Lleida, C/ Jaume II 69, E-25001 Lleida, Spain
2   Hesoft Group, Partida Bovà 15, 25196 LLeida, Spain
› Institutsangaben
Funding This work was supported by the Ministerio de Economía y Competitividad under contract TIN2014–53234-C2–2-R. The authors are members of the research group 2014-SGR163, funded by the Generalitat de Catalunya. Besides, this research is partly supported by the European Union FEDER (CAPAP-H5 network TIN2014–53522-REDT).

Summary

Background Hypertension or high blood pressure is on the rise. Not only does it affect the elderly but is also increasingly spreading to younger sectors of the population. Treating this condition involves exhaustive monitoring of patients. The current mobile health services can be improved to perform this task more effectively.

Objective To develop a useful, user-friendly, robust and efficient app, to monitor hypertensive patients and adapted to the particular requirements of hypertension.

Methods This work presents BPcontrol, an Android and iOS app that allows hypertensive patients to communicate with their health-care centers, thus facilitating monitoring and diagnosis. Usability, robustness and efficiency factors for BPcontrol were evaluated for different devices and operating systems (Android, iOS and system-aware). Furthermore, its features were compared with other similar apps in the literature.

Results BPcontrol is robust and user-friendly. The respective start-up efficiency of the Android and iOS versions of BPcontrol were 2.4 and 8.8 times faster than a system-aware app. Similar values were obtained for the communication efficiency (7.25 and 11.75 times faster for the Android and iOS respectively). When comparing plotting performance, BPcontrol was on average 2.25 times faster in the Android case. Most of the apps in the literature have no communication with a server, thus making it impossible to compare their performance with BPcontrol.

Conclusions Its optimal design and the good behavior of its facilities make BPcontrol a very promising mobile app for monitoring hypertensive patients.

Citation: Carrera A, Pifarré M, Vilaplana J, Cuadrado J, Solsona S, Mateo J, Solsona F. BPcontrol: a mobile app to monitor hypertensive patients



Publikationsverlauf

Eingereicht: 04. Dezember 2015

Angenommen: 02. Juni 2016

Publikationsdatum:
18. Dezember 2017 (online)

© 2016. Thieme. All rights reserved.

Schattauer GmbH

 
  • References

  • 1 Bray EP, Holder R, Mant J, McManus RJ. Does self monitoring reduce blood pressure? Meta-analysis with meta-regression of randomized controlled trials. Ann Med. 2010 published online.
  • 2 Hypertension in the very old; prevalence, awareness, treatment and control: a crosssectional populationbased study in a Spanish municipality. BMC Geriatrics. BMC series; 2009. 09 16.
  • 3 Logan AG, McIsaac WJ, Tisler A, Irvine MJ, Saunders A, Dunai A, Rizo CA, Feig DS, Hamill M, Trudel M, Cafazzo JA. Mobile Phone-Based Remote Patient Monitoring System for Management of Hypertension in Diabetic Patients. American Journal of Hypertension 2007; 20 (09) 942-948.
  • 4 Bobrie G, Chatellier G, Genes N, Clerson P, Vaur L, Vaisse B, Menard J, Mallion JM. Cardiovascular prognosis of masked hypertension detected by blood pressure self-measurement in elderly treated hypertensive patients. JAMA 2004; 291: 1342-1349.
  • 5 Ohkubo T, Imai Y, Tsuji I, Nagai K, Kato J, Kikuchi N, Nishiyama A, Aihara A, Sekino M, Kikuya M, Ito S, Satoh H, Hisamichi S. Home blood pressure measurement has a stronger predictive power for mortality than does screening blood pressure measurement: a population-based observation in Ohasama, Japan. J Hypertension 1998; 16 (07) 971-975.
  • 6 Pickering TG, 1 Miller NH, Ogedegbe G, Krakoff LR, Artinian NT, Goff D. 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. J Cardiovasc Nurs 2008; 23 (04) 299-323.
  • 7 Green BB, Cook AJ, Ralston JD, Fishman PA, Catz SL, Carlson J, Carrell D, Tyll L, Larson EB, Thompson RS. Effectiveness of Home Blood Pressure Monitoring, Web Communication, and Pharmacist Care on Hypertension Control: A Randomized Controlled Trial. JAMA 2008; 299 (24) 2857-2867.
  • 8 Vilaplana J, Solsona F, Abella F, Cuadrado J, Teixidó I, Mateo J, Rius J. H-PC: A Cloud Computing Tool for Supervising Hypertensive Patients. Journal of Supercomputing 2015; 71 (02) 591-612.
  • 9 Dickinson HO, Mason JM, Nicolson DJ, Campbell F, Beyer FR, Cook JV, Williams B, Ford GA. Lifestyle interventions to reduce raised blood pressure: a systematic review of randomized controlled trials. J Hypertens 2006; 24 (02) 215-233.
  • 10 Law MR, Morris JK, Wald NJ. 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 2009; 338: b1665.
  • 11 McManus RJ, Mant J, Bray EP, Holder R, Jones MI, Greenfield S, Kaambwa B, Banting M, Bryan S, Little P, Williams B, Hobbs FD. Telemonitoring and self-management in the control of hypertension (TASMINH2): a randomised controlled trial. The Lancet 2010; 376 9736 163-172.
  • 12 Ogedegbe G, Schoenthaler A. A systematic review of the eff ects of home blood pressure monitoring on medication adherence. J Clin Hypertens 2006; 08: 174-180.
  • 13 Patel B, Turban S, Anderson C, Charleston J, Miller E, Appel L. A Comparison of Web Sites Used to Manage and Present Home Blood Pressure Readings. J Clin Hypertens 2010; 12 (06) 389-395.
  • 14 Kang H, Park HA. A Mobile App for Hypertension Management Based on Clinical Practice Guidelines: Development and Deployment. JMIR Mhealth Uhealth 2016; 04 (01) e12.
  • 15 Logan AG, Irvine MJ, McIsaac WJ, Tisler A, Rossos PG, Easty A, Feig DS, Cafazzo JA. Effect of home blood pressure telemonitoring with self-care support on uncontrolled systolic hypertension in diabetics. Hypertension 2012; 60 (01) 51-57.
  • 16 Miyamoto SW, Henderson S, Young HM, Pande A. Tracking Health Data Is Not Enough:A Qualitative Exploration of the Role of Healthcare Partnerships and mHealth Technology to Promote Physical Activity and to Sustain Behavior Change. JMIR Mhealth Uhealth 2016; 04 (01) e5.
  • 17 Boulos MN, Brewer AC, Karimkhani C, Buller DB, Dellavalle RP. Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online J Public Health Inform 2014; 05 (03) e229.
  • 18 Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol 2010; 15 (01) 1-39.
  • 19 Abroms LC, Padmanabhan N, Thaweethai L, Phillips T. iPhone apps for smoking cessation: a content analysis. Am J Prev Med 2011; 40 (03) 279-285.
  • 20 Kumar N, Khunger M, Gupta A, Garg N. A content analysis of smartphone-based applications for hypertension management. Journal of the American Society of Hypertension 2015; 09 (02) 130-136.
  • 21 Vilaplana J, Solsona F, Abella F, Cuadrado J, Alves R, Mateo J. S-PC: An e-treatment application for management of smoke-quitting patients. Computer Methods and Programs in Biomedicine 2014; 115 (01) 33-45.
  • 22 Vilaplana J, Solsona F, Teixidó I, Mateo J, Abella F, Rius J. A queuing theory model for cloud computing. Journal of Supercomputing, Vol 2014; 06 (01) 492-507.
  • 23 Vilaplana J, Solsona F, Mateo J, Teixidó I. SLA-Aware Load Balancing in a Web-Based Cloud System over OpenStack. Lecture Notes in Computer Science 2014; 8377: 281-293.
  • 24 Mateo J, Vilaplana J, Pla LM, Lerida JL, Solsona F. A Green Strategy for Federated and Heterogeneous Clouds with Communicating Workloads. The Scientific World Journal 2014; 2014: 1-11.
  • 25 Martell N, Bertomeu V, Redon J, Galve E. Guides for the management of hypertension of 2013 ESH-ESC. European Society of Hipertension. 2013
  • 26 Miller RB. Response time in man-computer conversational transactions. Proc. AFIPS Fall Joint Computer Conference 1968; 33: 267-277