Exploring Intention on Continuous Use of Mobile Health Applications Designed by Persuasive Technology: “Adimsayar” Case Study

  • Seray Öney DoğanyiğitEmail author


Mobile applications for healthcare exist in large numbers today, but most users do not continue to use them after a short period of initial usage, and much remains unknown in regard to user habits. This study focused on the influential factors on motivation of behavior and intention of users toward continuous usage of health applications after downloading them. The study was planned in three sections. In the first stage, the user demographics; in the second, the application usage; and in the last stage, the factors that influence the intention of users on using the application were collected and analyzed. A hybrid model based on Ajzen’s theory of planned behavior and Fogg’s captology was proposed as the research model of this study. The findings revealed that as the users have more trust in the health application to promote their healthy behavior (behavioral attitude), they will have control over the application (perceived behavioral control) and higher tendency to continue using the applications, and the applications have more persuasive features (captology). Additionally, as the part of the hybrid model, trust dimension has been used as a moderator, since this variable had a significant role over the individuals’ intentions toward continuing to use the application. To be used in this study, Adımsayar application was introduced, which was designed to encourage healthy behavior.


Mobile health Persuasive technology Continuous use Captology Activity tracker 


  1. Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211CrossRefGoogle Scholar
  2. Ajzen I (2013) Theory of planned behaviour questionnaire. Measurement Instrument Database for the Social Science, s.l.
  3. Bandura A (1977) Self-efficacy: toward a unifying theory of behaviour change psychological review, vol 84. American Psychological Association, Washington, DC, pp 191–215Google Scholar
  4. Bandura A (1986) Social foundations of thought and action: a social cognitive theory. Prentice Hall, Englewood CliffsGoogle Scholar
  5. Becker S et al (2014) mHealth 2.0: experiences, possibilities, and perspectives. JMIR mHealth uHealth 2:1–9CrossRefGoogle Scholar
  6. Boland P (2007) Managing chronic disease through mobile persuasion. In: Fogg BJ, Eackles D (eds) Mobile persuasion: 20 perspectives on the future of behavior change. Standford Captology Media, Standford, pp 45–52Google Scholar
  7. CHIC (2011) Motivating patients to use smartphone health apps. Consumer Health Information Corporation, McLean. Accessed 28 Feb 2017
  8. Cohen JW (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, HillsdaleGoogle Scholar
  9. Consolvo S, Paulos E, Smith I (2007) Mobile persuasion for everyday behavior change. In: Fogg BJ, Eackles D (eds) Mobile persuasion: 20 perspectives on the future of behavior change. Standford Captology Media, Standford, pp 77–84Google Scholar
  10. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS quarterly, vol 13. University of Minnesota, MIS Research Center, Minneapolis, pp 319–340Google Scholar
  11. Doganyigit SO, Yilmaz E (2015) Mobile health applications user trends in Turkey. J Mass Commun 5(1):44–49Google Scholar
  12. Fitzgerald M, McClelland T (2017) What makes a mobile app successful in supporting health behaviour change? Health Educ J 76(3):373CrossRefGoogle Scholar
  13. Fogg BJ (2003) Persuasive technology: using computers to change what we think and do. Morgan Kaufmann, San FranciscoGoogle Scholar
  14. Fogg BJ (2009) Creating persuasive technologies: an eight-step design process. In: Proceedings of the 4th international conference on persuasive technology, article 44, pp 26–29Google Scholar
  15. Francis JJ et al (2004) Constructing questionnaires based on the theory of planned behaviour: a manual for health services researchers. City University of London, LondonGoogle Scholar
  16. Gefen D (2002) Customer loyalty in E-commerce. J Assoc Inf Syst 3:27–51Google Scholar
  17. Hair JF Jr, Black WC, Babin BJ et al (2010) Multivariate data analysis: a global perspective, 7th edn. Pearson Education Inc., Upper Saddle RiverGoogle Scholar
  18. Halko S, Kientz JA (2010) Personality & persuasive technology: an exploratory study on health-promoting mobile applications. In: Hasle TPP, Oinas-Kukkonen H (eds) Persuasive technology: 5th international conference. Springer Berlin Heidelberg, Copenhagen, pp 150–161CrossRefGoogle Scholar
  19. Harri OK and Harjumaa M (2008) Towards deeper understanding of persuasion in software and information systems. Paper presented first international conference on advances in computer-human interaction, March 2008, pp 200–205Google Scholar
  20. Klasnja P, Consolvo S, Pratt W (2011) How to evaluate technologies for health behavior change in HCI research. In: CHI’11 proceedings of the SIGCHI conference on human factors in computing systems, May 2011, Vancouver, ACM, pp 3063–3072Google Scholar
  21. Mason CH, Perreault WD Jr (1991) Collinearity, power, and interpretation of multiple regression analysis. J Mark Res 28(3):268–280CrossRefGoogle Scholar
  22. McGraa KL (2010) The effects of persuasive motivational text messaging on adherence to diet and exercise programs across different personality traits. Fielding Graduate University, Santa BarbaraGoogle Scholar
  23. Nickel P, Spahn A (2012) Trust, discourse ethics, and persuasive technology. In: Ragnemalm EL, Magnus B (eds) The 7th international conference on persuasive technology, PERSUASIVE 2012. Linköping University Electronic Press, LinköpingGoogle Scholar
  24. Pallant J (2007) SPSS survival manual a step by step guide to data analysis using SPSS for windows, 3rd edn. McGraw Hill Open University Press, New YorkGoogle Scholar
  25. Pavlou PA, Fygenson M (2006) Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Q 30(1):115–143CrossRefGoogle Scholar
  26. Research2guidance (2012) Mobile health application landscape 2011–2016. Accessed 28 Feb 2017
  27. Tavşancil E (2002) Tutumlarin ölçülmesi ve SPSS ile veri analizi. Nobel Press, AnkaraGoogle Scholar
  28. Tull DS, Hawkins DI (1993) Marketing research: measurement and method, 6th edn. Prentice Hall of India, New DelhiGoogle Scholar
  29. Zhuang D (2013) Designing for behavioral change in health. http://wwwuxboothcom/articles/designing-for-behavioral-change-in-health/. Accessed Feb 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Istanbul Bilgi UniversityIstanbulTurkey

Personalised recommendations