Putting the Long-Term into Behavior Change

  • Harmen de WeerdEmail author
  • Nick Degens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11385)


Behavior change is a topic that is of great interest to many people. People can use apps to exercise more, eat healthier, or learn a new skill, but and digital interventions and games are also used by policy makers and companies to create a safe environment for the general public or to increase sales. Given this interest in behavior change, it is not surprising that this topic has seen a lot of interest from the scientific community. This has resulted in a wide range of theories and techniques to bring about behavior change. However, maintaining behavior change is rarely addressed, and as a result poorly understood. In this paper, we take a first step in the design of digital interventions for long-term behavior change by placing a range of behavior change techniques on a long-term behavior change timeline.


Behavior change Long-term effects Behavior change techniques 


  1. 1.
    Allen, K.: Chronic nailbiting: a controlled comparison of competing response and mild aversion treatments. Behav. Res. Ther. 34(3), 269–272 (1996)Google Scholar
  2. 2.
    Anderson, B., Jenkins, J., Vance, A., Kirwan, C., Eargle, D.: Your memory is working against you: how eye tracking and memory explain habituation to security warnings. Decis. Support Syst. 92, 3–13 (2016)Google Scholar
  3. 3.
    Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84(2), 191–215 (1977)Google Scholar
  4. 4.
    Bandura, A., Cervone, D.: Differential engagement of self-reactive influences in cognitive motivation. Organ. Behav. Hum. Decis. Process. 38(1), 92–113 (1986)Google Scholar
  5. 5.
    Bouton, M.: Why behavior change is difficult to sustain. Prev. Med. 68, 29–36 (2014)Google Scholar
  6. 6.
    Burch, N.: The Four Stages for Learning Any New Skill. Gordon Training International, Solana Beach (1970)Google Scholar
  7. 7.
    Cameirão, M., i Badia, S., Zimmerli, L., Oller, E., Verschure, P.: The rehabilitation gaming system: a virtual reality based system for the evaluation and rehabilitation of motor deficits. In: Virtual Rehabilitation, pp. 29–33 (2007)Google Scholar
  8. 8.
    Carraro, N., Gaudreau, P.: Spontaneous and experimentally induced action planning and coping planning for physical activity: a meta-analysis. Psychol. Sport Exerc. 14(2), 228–248 (2013)Google Scholar
  9. 9.
    Conroy, D., Yang, C., Maher, J.: Behavior change techniques in top-ranked mobile apps for physical activity. Am. J. Prev. Med. 46(6), 649–652 (2014)Google Scholar
  10. 10.
    Gardner, B.: Habit as automaticity, not frequency. Eur. Health Psychol. 14(2), 32–36 (2012)Google Scholar
  11. 11.
    Gneezy, U., Rustichini, A.: A fine is a price. J. Leg. Stud. 29(1), 1–17 (2000)Google Scholar
  12. 12.
    Gourlan, M., et al.: Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomised controlled trials. Health Psychol. Rev. 10(1), 50–66 (2016)Google Scholar
  13. 13.
    Heyman, J., Ariely, D.: Effort for payment: a tale of two markets. Psychol. Sci. 15(11), 787–793 (2004)Google Scholar
  14. 14.
    Karppinen, P., et al.: Persuasive user experiences of a health Behavior Change Support System: a 12-month study for prevention of metabolic syndrome. Int. J. Med. Inform. 96, 51–61 (2016)Google Scholar
  15. 15.
    Kessels, L., Ruiter, R., Wouters, L., Jansma, B.: Neuroscientific evidence for defensive avoidance of fear appeals. Int. J. Psychol. 49(2), 80–88 (2014)Google Scholar
  16. 16.
    Kim, T., Werbach, K.: More than just a game: ethical issues in gamification. Ethics Inf. Technol. 18(2), 157–173 (2016)Google Scholar
  17. 17.
    Kraiger, K., Ford, J., Salas, E.: Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. J. Appl. Psychol. 78(2), 311–328 (1993)Google Scholar
  18. 18.
    Kwasnicka, D., Dombrowski, S., White, M., Sniehotta, F.: Theoretical explanations for maintenance of behaviour change: a systematic review of behaviour theories. Health Psychol. Rev. 10(3), 277–296 (2016)Google Scholar
  19. 19.
    Lawpoolsri, S., Li, J., Braver, E.: Do speeding tickets reduce the likelihood of receiving subsequent speeding tickets? A longitudinal study of speeding violators in Maryland. Traffic Inj. Prev. 8(1), 26–34 (2007)Google Scholar
  20. 20.
    Lazar, A., Koehler, C., Tanenbaum, J., Nguyen, D.: Why we use and abandon smart devices. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 635–646 (2015)Google Scholar
  21. 21.
    Ma, F., et al.: Promoting honesty in young children through observational learning. J. Exp. Child Psychol. 167, 234–245 (2018)Google Scholar
  22. 22.
    Michie, S., van Stralen, M., West, R.: The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6(1), 42 (2011)Google Scholar
  23. 23.
    Michie, S., et al.: The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann. Behav. Med. 46(1), 81–95 (2013)Google Scholar
  24. 24.
    Nettle, D., Harper, Z., Kidson, A., Stone, R., Penton-Voak, I., Bateson, M.: The watching eyes effect in the Dictator Game: it’s not how much you give, it’s being seen to give something. Evol. Hum. Behav. 34(1), 35–40 (2013)Google Scholar
  25. 25.
    Nolan, J., Schultz, P., Cialdini, R., Goldstein, N., Griskevicius, V.: Normative social influence is underdetected. Pers. Soc. Psychol. Bull. 34(7), 913–923 (2008)Google Scholar
  26. 26.
    Orbell, S., Verplanken, B.: The automatic component of habit in health behavior: habit as cue-contingent automaticity. Health Psychol. 29(4), 374–383 (2010)Google Scholar
  27. 27.
    Ploderer, B., Smith, W., Pearce, J., Borland, R.: A mobile app offering distractions and tips to cope with cigarette craving: a qualitative study. JMIR mHealth uHealth 2(2), e23 (2014)Google Scholar
  28. 28.
    van den Putte, B., Yzer, M., Willemsen, M., de Bruijn, G.: The effects of smoking self-identity and quitting self-identity on attempts to quit smoking. Health Psychol. 28(5), 535–544 (2009)Google Scholar
  29. 29.
    Schunk, D., Hanson, A.: Peer models: influence on children’s self-efficacy and achievement. J. Educ. Psychol. 77(3), 313–322 (1985)Google Scholar
  30. 30.
    Shih, P., Han, K., Poole, E., Rosson, M., Carroll, J.: Use and adoption challenges of wearable activity trackers. In: iConference 2015 Proceedings (2015)Google Scholar
  31. 31.
    Sohn, T., Li, K.A., Lee, G., Smith, I., Scott, J., Griswold, W.G.: Place-its: a study of location-based reminders on mobile phones. In: Beigl, M., Intille, S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 232–250. Springer, Heidelberg (2005). Scholar
  32. 32.
    Stawarz, K., Cox, A., Blandford, A.: Beyond self-tracking and reminders: designing smartphone apps that support habit formation. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2653–2662. ACM (2015)Google Scholar
  33. 33.
    Tannenbaum, M., et al.: Appealing to fear: a meta-analysis of fear appeal effectiveness and theories. Psychol. Bull. 141(6), 1178–1204 (2015)Google Scholar
  34. 34.
    Ubel, P., Jepson, C., Baron, J.: The inclusion of patient testimonials in decision aids: effects on treatment choices. Med. Decis. Making 21(1), 60–68 (2001)Google Scholar
  35. 35.
    Webb, T., Joseph, J., Yardley, L., Michie, S.: Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J. Med. Internet Res. 12(1), e4 (2010)Google Scholar
  36. 36.
    WeWantToKnow (2011). Dragonbox Algebra.
  37. 37.
    Wildeboer, G., Kelders, S., van Gemert-Pijnen, J.: The relationship between persuasive technology principles, adherence and effect of web-based interventions for mental health: a meta-analysis. Int. J. Med. Inform. 96, 71–85 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Research Group User-Centered DesignHanze University of Applied SciencesGroningenThe Netherlands

Personalised recommendations