Advertisement

Mobile Technology Improves Therapy-Adherence Rates in Elderly Patients Undergoing Rehabilitation—A Crossover Design Study

  • A. MertensEmail author
  • S. Becker
  • S. Theis
  • P. Rasche
  • M. Wille
  • C. Bröhl
  • L. Finken
  • C. Schlick
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 482)

Abstract

In this publication the results of an empirical study are analyzes regarding the research question if a mobile application on a tablet computer, to support the drug intake and vital sign parameter documentation, affects adherence of elderly patients. For the achievement in the management of patients with hypertension adherence of their medication is essential. Patients with no prior knowledge of tablet computers and a coronary heart disease were included. All Patients were instructed personally into the mobile application “Medication Plan”, installed on an Apple iPad™. This study was performed in a crossover design with three sequences. The first sequence is the initial phase, followed by the interventional phase (28 days of using the app system) and at least the comparative phase (28 days of using a paper diary). The interventional and comparative phases were conducted alternately. Altogether, 24 patients (12 male; mean age 73.8 years) were registered. The subjectively assessed adherence (A 14 scale) was 50.0 before the study started (SD 3.44). After the enforcement of both interventions there was a significant increase, which was more pronounced after the intervention phase (54.0, SD 2.01) than the comparative phase (52.6, SD 2.49) (for all pairs p < 0.001). Furthermore, the medical conditions, or the number of drug intakes per day had no effect on the subjective adherence. For both blood pressure recordings (p < 0.001) and medication intake (p = 0.033) the obtained logging data showed a significantly stronger adherence for the medication-app than the paper diary system. The majority of participants (n = 22) denoted that they would like to use the medication-app in everyday life and do not need any further assistance. A mobile app for medication adherence strengthened objectively and subjectively metered adherence of elderly users folding rehabilitation.

Keywords

Drug therapy Self-management Therapy adherence Elderly patients Mobile application Tablet computer 

References

  1. 1.
    Heron, M.: Deaths: leading causes for 2009. Natl. Vital. Stat. Rep. 2012(61), 1–96 (2009)Google Scholar
  2. 2.
    Heran, B.S., Chen, J.M., Ebrahim, S., Moxham, T., Oldridge, N., Rees, K., Thompson, D.R., Taylor, R.S.: Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst. Rev. 2011(7). Art. No.: CD001800 (2011)Google Scholar
  3. 3.
    Osterberg, L., Blaschke, T.: Adherence to medication. N. Engl. J. Med. 353, 487–497 (2005)CrossRefGoogle Scholar
  4. 4.
    Vrijens, B., Vincze, G., Kristanto, P., Urquhart, J., Burnier, M.: Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ 336, 1114–1117 (2005)CrossRefGoogle Scholar
  5. 5.
    Diamantidis, C.J., Becker, S.: Health information technology (IT) to improve the care of patients with chronic kidney disease (CKD). BMC Nephrol. 15, 7 (2014)CrossRefGoogle Scholar
  6. 6.
    Becker, S., Miron-Shatz, T., Schumacher, N., Krocza, J., Diamantidis, C., Albrecht, U.V.: mHealth 2.0: experiences, possibilities, and perspectives. JMIR mHealth and uHealth 2(2), e24 (2014)Google Scholar
  7. 7.
    Grindrod, K.A., Li, M., Gates, A.: evaluating user perceptions of mobile medication management applications with older adults: a usability study. JMIR Mhealth Uhealth 2(1), e11 (2014)Google Scholar
  8. 8.
    Becker, S., Kribben, A., Meister, S., Diamantidis, C.J., Unger, N., Mitchell, A.: User profiles of a smartphone application to support drug adherence–experiences from the iNephro project. PLoS ONE 8, e78547 (2013)CrossRefGoogle Scholar
  9. 9.
    Becker, S., Brandl, C., Meister, S., Nagel, E., Miron-Shatz, T., Mitchell, A., Kribben, A., Mertens, A.: Demographic and health related data of users of a mobile application to support drug adherence is associated with usage duration and intensity. PLoS ONE 10(1), e0116980 (2015)CrossRefGoogle Scholar
  10. 10.
    Sengpiel, M., Struve, D., Secombe, C., Wong, Y.K.: Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcareGoogle Scholar
  11. 11.
    Jank, S., Bertsche, T., Schellberg, D., Herzog, W., Haefeli, W.E.: The A14-scale: development and evaluation of a questionnaire for assessment of adherence and individual barriers. Pharm. World Sci. 31(4), 426–431 (2009)CrossRefGoogle Scholar
  12. 12.
    Karrer, K., Glaser, C., Clemens, C., Bruder, C.: Technikaffinität erfassen – der Fragebogen TA-EG. In: Lichtenstein, A., Stößel, C., Clemens, C. (Hrsg.) Der Mensch im Mittelpunkt technischer Systeme, 8. Berliner Werkstatt Mensch-Maschine-Systeme, ZMMS Spektrum, vol. 22, no. 29, pp. 196–201. VDI Verlag GmbH, Düsseldorf (2009)Google Scholar
  13. 13.
    Leuteritz, J.P., Widlroither, H., Klüh, M.: Multi-level validation of the ISOMetrics questionnaire based on qualitative and quantitative data obtained from a conventional usability test. In: Proceedings of the 13th International Human-Computer Interaction Conference (HCI ‘09), pp. 304–313. LNCS 5610 (2009)Google Scholar
  14. 14.
    Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., et al.: The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLOS Med. 10(1), e1001363 (2013)CrossRefGoogle Scholar
  15. 15.
    Lester, R.T., Ritvo, P., Mills, E.J., Kariri, A., Karanja, S., et al.: Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet 376(9755), 1838–1845 (2010)Google Scholar
  16. 16.
    Mulvaney, S.A., Anders, S., Smith, A.K., Pittel, E.J., Johnson, K.B.: A pilot test of a tailored mobile and web-based diabetes messaging system for adoles-cents. J. Telemed. Telecare. 18(2), 115–118 (2012) (Epub 2012/03/03)Google Scholar
  17. 17.
    Vervloet, M., van Dijk, L., Santen-Reestman, J., van Vlijmen, B., van Wingerden, P., et al.: SMS reminders improve adherence to oral medication in type 2 diabetes patients who are real time electronically monitored. Int. J. Med. Inform. 81(9), 594–604 (2012)CrossRefGoogle Scholar
  18. 18.
    YR Besitz von mobilen Endgeräten in der Schweiz nach Altersgruppen (2013)Google Scholar
  19. 19.
    Steele, R., Lo, A., Secombe, C., Wong, Y.K.: Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. Int. J. Med. Inform 78(12), 788–801 (2009)CrossRefGoogle Scholar
  20. 20.
    Sarkar, U., Karter, A.J., Liu, J.Y., Adler, N.E., Nguyen, R., Lopez, A., Schillinger, D.: Social disparities in internet patient portal use in diabetes: evidence that the digital divide extends beyond access. J. Am. Med. In-form Assoc. 18(3), 318–321 (2011)CrossRefGoogle Scholar
  21. 21.
    Lorence, D.P., Park, H., Fox, S.: Racial disparities in health information access: resilience of the digital divide. J. Med. Syst. 30(4), 241–249 (2006)CrossRefGoogle Scholar
  22. 22.
    Rogers, E.M.: Diffusion of Innovations, 5th edn. Free Press, New York (2003)Google Scholar
  23. 23.
    Wetzels, G.E., Nelemans, P.J., Schouten, J.S., van Wijk, B.L., Prins, M.H.: All that glisters is not gold: a comparison of electronic monitoring versus filled prescriptions—an observational study. BMC Health Serv Res. 6, 8 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • A. Mertens
    • 1
    Email author
  • S. Becker
    • 1
  • S. Theis
    • 1
  • P. Rasche
    • 1
  • M. Wille
    • 1
  • C. Bröhl
    • 1
  • L. Finken
    • 1
  • C. Schlick
    • 1
  1. 1.Institute of Industrial Engineering and ErgonomicsRWTH Aachen UniversityAachenGermany

Personalised recommendations