Crowd-Designed Motivation: Combining Personality and the Transtheoretical Model

  • Roelof A. J. de VriesEmail author
  • Khiet P. Truong
  • Vanessa Evers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9638)


Current approaches to design motivational technology for behavior change focus on either tailoring motivational strategies to individual preferences or on implementing strategies from behavior change theory. Our goal is to combine these two approaches and translate behavior change theory to text messages, tailored to personality. To this end, we conducted an online survey with 481 participants exploring the relationship between behavior change theory (the Transtheoretical Model) and personality in the context of physical activity. Our results show that (1) people’s personalities correlate with their stage of change and (2) people’s personalities and their stages of change correlate to preferences for certain processes of change. We discuss the implications of the results for designing motivational technology.


Behavior change Persuasion strategies Personality Processes of change Stages of change Transtheoretical model 



This research was funded by COMMIT/ and is part of the P3 project SenseI: Sensor-Based Engagement for Improved Health. We would like to thank Maartje de Graaf for her input.


  1. 1.
    Hekler, E.B., Klasnja, P., Froehlich, J.E., Buman, M.P.: Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, PP. 3307–3316. ACM (2013)Google Scholar
  2. 2.
    Noar, S.M., Benac, C.N., Harris, M.S.: Does tailoring matter? meta-analytic review of tailored print health behavior change interventions. Psychol. Bull. 133(4), 673–693 (2007)CrossRefGoogle Scholar
  3. 3.
    Arteaga, S.M., Kudeki, M., Woodworth, A., Kurniawan, S.: Mobile system to motivate teenagers’ physical activity. In: Proceedings of the 9th International Conference on Interaction Design and Children, pp. 1–10. ACM (2010)Google Scholar
  4. 4.
    Chatterjee, S., Price, A.: Healthy living with persuasive technologies: framework, issues, and challenges. J. Am. Med. Inform. Assoc. 16(2), 171–178 (2009)CrossRefGoogle Scholar
  5. 5.
    Prochaska, J.O., DiClemente, C.C.: Stages and processes of self-change of smoking: toward an integrative model of change. J. Consult. Clin. Psychol. 51(3), 390 (1983)CrossRefGoogle Scholar
  6. 6.
    Spencer, L., Adams, T.B., Malone, S., Roy, L., Yost, E.: Applying the transtheoretical model to exercise: a systematic and comprehensive review of the literature. Health Promot. Pract. 7(4), 428–443 (2006)CrossRefGoogle Scholar
  7. 7.
    Marcus, B.H., Rossi, J.S., Selby, V.C., Niaura, R.S., Abrams, D.B.: The stages and processes of exercise adoption and maintenance in a worksite sample. Health Psychol. 11(6), 386 (1992)CrossRefGoogle Scholar
  8. 8.
    Lacroix, J., Saini, P., Goris, A.: Understanding user cognitions to guide the tailoring of persuasive technology-based physical activity interventions. In: Proceedings of the 4th International Conference on Persuasive Technology, p. 9. ACM (2009)Google Scholar
  9. 9.
    Kaptein, M., De Ruyter, B., Markopoulos, P., Aarts, E.: Adaptive persuasive systems: a study of tailored persuasive text messages to reduce snacking. ACM Trans. Interact. Intell. Syst. (TIIS) 2(2), 10 (2012)Google Scholar
  10. 10.
    Alkış, N., Temizel, T.T.: The impact of individual differences on influence strategies. Personality Individ. Differ. 87, 147–152 (2015)CrossRefGoogle Scholar
  11. 11.
    Kaptein, M., Markopoulos, P., de Ruyter, B., Aarts, E.: Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. Int. J. Hum. Comput. Stud. 77, 38–51 (2015)CrossRefGoogle Scholar
  12. 12.
    Nigg, C.R., Geller, K.S., Motl, R.W., Horwath, C.C., Wertin, K.K., Dishman, R.K.: A research agenda to examine the efficacy and relevance of the transtheoretical model for physical activity behavior. Psychol. Sport Exercise 12(1), 7–12 (2011)CrossRefGoogle Scholar
  13. 13.
    Goldberg, L.R.: The development of markers for the big-five factor structure. Psychol. Assess. 4(1), 26 (1992)CrossRefGoogle Scholar
  14. 14.
    Costa, P.T., McCrae, R.R.: Normal personality assessment in clinical practice: The NEO personality inventory. Psychol. Assess. 4(1), 5 (1992)CrossRefGoogle Scholar
  15. 15.
    Rhodes, R.E., Courneya, K.S., Jones, L.W.: Personality and social cognitive influences on exercise behavior: adding the activity trait to the theory of planned behavior. Psychol. Sport Exerc. 5(3), 243–254 (2004)CrossRefGoogle Scholar
  16. 16.
    McCrae, R.R., Costa Jr., P.T.: A five-factor theory of personality. Handbook of personality: Theory and research 2, 139–153 (1999)Google Scholar
  17. 17.
    Cole-Lewis, H., Kershaw, T.: Text messaging as a tool for behavior change in disease prevention and management. Epidemiol. Rev. 32(1), 56–69 (2010)CrossRefGoogle Scholar
  18. 18.
    Michie, S., Johnston, M., Francis, J., Hardeman, W., Eccles, M.: From theory to intervention: mapping theoretically derived behavioural determinants to behaviour change techniques. Appl. psychol. 57(4), 660–680 (2008)CrossRefGoogle Scholar
  19. 19.
    Gallagher, P., Yancy, W.S., Denissen, J.J.A., Kühnel, A., Voils, C.I.: Correlates of daily leisure-time physical activity in a community sample: Narrow personality traits and practical barriers. Health Psychol. Official J. Div. Health Psychol. Am. Psychol. Assoc. 32(12), 1227–1235 (2013)Google Scholar
  20. 20.
    Hoyt, A.L., Rhodes, R.E., Hausenblas, H.A., Giacobbi, P.R.: Integrating five-factor model facet-level traits with the theory of planned behavior and exercise. Psychol. Sport Exerc. 10(5), 565–572 (2009)CrossRefGoogle Scholar
  21. 21.
    Rhodes, R.E., Smith, N.E.I.: Personality correlates of physical activity: a review and meta-analysis. Br. J. Sports Med. 40(12), 958–965 (2006)CrossRefGoogle Scholar
  22. 22.
    Courneya, K.S., Hellsten, L.A.M.: Personality correlates of exercise behavior, motives, barriers and preferences: An application of the five-factor model. Personality Indiv. Differ. 24(5), 625–633 (1998)CrossRefGoogle Scholar
  23. 23.
    Halko, S., Kientz, J.: Personality and persuasive technology: an exploratory study on health-promoting mobile applications. In: Persuasive technology, pp. 150–161 (2010)Google Scholar
  24. 24.
    Ingledew, D.K., Markland, D.: The role of motives in exercise participation. Psychol. Health 23(7), 807–828 (2008)CrossRefGoogle Scholar
  25. 25.
    Ferron, M., Massa, P.: Transtheoretical model for designing technologies supporting an active lifestyle. In: Proceedings of the Biannual Conference of the Italian Chapter of SIGCHI, p. 7. ACM (2013)Google Scholar
  26. 26.
    Latimer, A.E., Brawley, L.R., Bassett, R.L.: A systematic review of three approaches for constructing physical activity messages: what messages work and what improvements are needed? Int. J. Behav. Nutr. Phys. Act. 7, 36–53 (2010)CrossRefGoogle Scholar
  27. 27.
    Hirsh, J.B., Kang, S.K., Bodenhausen, G.V.: Personalized persuasion: tailoring persuasive appeals to recipients’ personality traits. Psychol. Sci. 23(6), 578–581 (2012)CrossRefGoogle Scholar
  28. 28.
    Adnan, M., Mukhtar, H., Naveed, M.: Persuading students for behavior change by determining their personality type. In: 2012 15th International Multitopic Conference (INMIC), pp. 439–449 (2012)Google Scholar
  29. 29.
    De Vries, R.A.J., Truong, K.P., Kwint, S., Drossaert, C.H.C., Evers, V.: Crowd-designed motivation: Motivational messages for exercise adherence based on behavior change theory. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (in press, 2016)Google Scholar
  30. 30.
    Norman, G., Benisovich, S., Nigg, C., Rossi, J.: Examining three exercise staging algorithms in two samples. In: 19th Annual Meeting of the Society of Behavioral Medicine (1998)Google Scholar
  31. 31.
    Nigg, C., Norman, G., Rossi, J., Benisovich, S.: Processes of exercise behavior change: Redeveloping the scale. Ann. Behav. Med. 21, S79 (1999)Google Scholar
  32. 32.
    Bogg, T.: Conscientiousness, the transtheoretical model of change, and exercise: a neo-socioanalytic integration of trait and social-cognitive frameworks in the prediction of behavior. J. Pers. 76(4), 775–802 (2008)CrossRefGoogle Scholar
  33. 33.
    Mason, W., Suri, S.: Conducting behavioral research on amazon’s mechanical turk. Behav. Res. Methods 44(1), 1–23 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Roelof A. J. de Vries
    • 1
    Email author
  • Khiet P. Truong
    • 1
  • Vanessa Evers
    • 1
  1. 1.University of TwenteEnschedeThe Netherlands

Personalised recommendations