Ontological Review of Persuasion Support Systems (PSS) for Health Behavior Change through Physical Activity

  • Khin Than WinEmail author
  • Arkalgud Ramaprasad
  • Thant Syn
Patient Facing Systems
Part of the following topical collections:
  1. Patient Facing Systems


Persuasion Support Systems (PSS) for health behavior change can play an important role in promoting health and well-being through physical activity. It is an emerging application at the crossroad between information systems, persuasion, and healthcare. We propose an ontology to systematically and systemically describe the construct of PSS for health behavior change. The ontology deconstructs the construct into its constituent dimensions and elements, and assembles them into a complete, parsimonious description of the same. We then map the corpus of literature on PSS for health behavior change through physical activity onto the ontology. The resulting ontological map highlights the research topics that are highly- and lightly-emphasized, as well as those with little or no emphasis. It illuminates the landscape of research in the corpus; it highlights biases in emphases that can help and hinder the advancement of the corpus. It can be used to develop a roadmap for future research.


Persuasion Health behavior change Ontology Physical activity 


Compliance with ethical standards

Conflict of interest

All authors declare that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Klecun-Dabrowska, E., and Cornford, T., Telehealth acquires meanings: information and communication technologies within health policy. Inf. Syst. J. 10(1):41–63, 2000. Scholar
  2. 2.
    Boonstra, A., and Van Offenbeek, M., Towards consistent modes of e-health implementation: structurational analysis of a telecare programme's limited success. Inf. Syst. J. 20(6):537–561, 2010. Scholar
  3. 3.
    Chatterjee, S., and Price, A., Healthy Living with Persuasive Technologies: Framework, Issues, and Challenges. J. Am. Med. Inform. Assoc. 16(2):171–178, 2009. Scholar
  4. 4.
    Kaptein, M., Markopoulos, P., de Ruyter, B., and Aarts, E., Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. Int J Human-Computer Stud 77:38–51, 2015. Scholar
  5. 5.
    Daud, N. A., Sahari@Ashaari, N., and Muda, Z., An Initial Model of Persuasive Design in Web based Learning Environment. Procedia Tech 11:895–902, 2013. Scholar
  6. 6.
    Looije, R., Neerincx, M. A., and Cnossen, F., Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors. Int J Human-Computer Stud 68(6):386–397, 2010. Scholar
  7. 7.
    Matthews, J., Win, K. T., Oinas-Kukkonen, H., and Freeman, M., Persuasive Technology in Mobile Applications Promoting Physical Activity: a Systematic Review. J. Med. Syst. 40(3):72, 2016. Scholar
  8. 8.
    Morrison, L. G., Yardley, L., Powell, J., and Michie, S., What Design Features Are Used in Effective e-Health Interventions? A Review Using Techniques from Critical Interpretive Synthesis. Telemed e-Health 18(2):137–144, 2012. Scholar
  9. 9.
    Doupi, P., and van der Lei, J., Design and implementation considerations for a personalized patient education system in burn care. Int. J. Med. Inform. 74(2–4):151–157, 2005. Scholar
  10. 10.
    Clayman, M. L., Boberg, E. W., and Makoul, G., The use of patient and provider perspectives to develop a patient-oriented website for women diagnosed with breast cancer. Patient Educ. Couns. 72(3):429–435, 2008. Scholar
  11. 11.
    Win, K. T., Hassan, N. M., Oinas-Kukkonen, H., and Probst, Y., Online Patient Education for Chronic Disease Management: Consumer Perspectives. J. Med. Syst. 40(4):88, 2016. Scholar
  12. 12.
    Ferney, S. L., and Marshall, A. L., Website physical activity interventions: preferences of potential users. Health Educ. Res. 21(4):560–566, 2006. Scholar
  13. 13.
    DeGuzman, M. A., and Ross, M. W., Assessing the application of HIV and AIDS related education and counselling on the Internet. Patient Educ. Couns. 36(3):209–228, 1999. Scholar
  14. 14.
    Gosselin, P., and Poitras, P., Use of an internet "viral" marketing software platform in health promotion. J. Med. Internet Res. 10(4):e47, 2008. Scholar
  15. 15.
    Rezailashkajani, M., Roshandel, D., Ansari, S., and Zali, M. R., A web-based patient education system and self-help group in Persian language for inflammatory bowel disease patients. Int. J. Med. Inform. 77(2):122–128, 2008. Scholar
  16. 16.
    Yamout, S. Z., Glick, Z. A., Lind, D. S., Monson, R. A. Z., and Glick, P. L., Using social media to enhance surgeon and patient education and communication. Bull Am College Surgeons 96(7):7–15, 2011.Google Scholar
  17. 17.
    Ramaprasad, A., Syn, T., Strong and Meaningful Use of Healthcare Information Systems (HIS). In: Bienkiewicz M, Verdier C, Plantier G, Schultz T, Fred A, Gamboa H (eds) Proceedings of the International Conference on Health Informatics (BIOSTEC 2014). SciTePress, Angers, pp. 381–386, 2014. 10.5220/0004870303810386Google Scholar
  18. 18.
    Gruber, T. R., Ontology. In: Liu, L., Özsu, M. T. (Eds), Encyclopedia of Database Systems. New York: Springer-Verlag, 2008, 1963–1965.Google Scholar
  19. 19.
    Gruber, T. R., Toward Principles for the Design of Ontologies Used for Knowledge Sharing. Int J Human-Computer Stud 43(5–6):907–928, 1995. Scholar
  20. 20.
    Cimino, J. J., In defense of the Desiderata. J. Biomed. Inform. 39(3):299–306, 2006. Scholar
  21. 21.
    Quine, W. V. O., From a Logical Point of View. Second, revised edn. Boston: Harvard University Press, 1961.Google Scholar
  22. 22.
    Oinas-Kukkonen, H., Harjumaa, M., Towards deeper understanding of persuasion in software and information systems. In: Proceedings of the 1st International Conference on Advances in Computer-Human Interaction. IEEE, pp 200–205, 2008.Google Scholar
  23. 23.
    Fogg, B. J., Persuasive Technology: Using Computers to Change What We Think and Do. Persuasive Technology. San Francisco: Morgan Kaufmann, 2003. 10.1016/B978-1-55860-643-2.X5000-8Google Scholar
  24. 24.
    Prochaska, J. O., Butterworth, S., Redding, C. A., Burden, V., Perrin, N., Leo, M., Flaherty-Robb, M., and Prochaska, J. M., Initial efficacy of MI, TTM tailoring and HRI’s with multiple behaviors for employee health promotion. Prev. Med. 46(3):226–231, 2008. Scholar
  25. 25.
    Malik, N. A., Zhang, J., Lam, O. L. T., Jin, L., and McGrath, C., Effectiveness of computer-aided learning in oral health among patients and caregivers: a systematic review. J. Am. Med. Inform. Assoc. 24(1):209–217, 2017. Scholar
  26. 26.
    Ramaprasad, A., Syn, T., Thirumalai, M., An Ontological Map for Meaningful Use of Healthcare Information Systems (MUHIS). In: Bienkiewicz M, Verdier C, Plantier G, Schultz T, Fred A, Gamboa H (eds) Proceedings of the International Conference on Health Informatics (BIOSTEC 2014). Angers: SciTePress, pp. 16–26, 2014. 10.5220/0004734500160026Google Scholar
  27. 27.
    Ramaprasad, A., Syn, T., Thirumalai, M., Ontological Analysis of Meaningful Use of Healthcare Information Systems (MUHIS) Requirements and Practice. In: Plantier G, Schulz T, Fred A, Gamboa H (eds) Biomedical Engineering Systems and Technologies, vol 511. Communications in Computer and Information Science. Cham: Springer International Publishing, pp. 315–330, 2015. 10.1007/978-3-319-26129-4_21Google Scholar
  28. 28.
    Ramaprasad, A., and Syn, T., Ontological Meta-Analysis and Synthesis. Comm Assoc Inform Syst 37(7):138–153, 2015.Google Scholar
  29. 29.
    Brennan, L., Voros, J., and Brady, E., Paradigms at play and implications for validity in social marketing research. J Soc Market 1(2):100–119, 2011. Scholar
  30. 30.
    Horn, B. R., Lee, I.-H., Toward integrated interdisciplinary information and communication sciences: a general systems perspective. In: Proceedings of the 22nd Hawaii International Conference on System Sciences (HICSS 1989), vol 4. Kailua-Kona: IEEE, pp 244–255, 1989. 10.1109/HICSS.1989.48129Google Scholar
  31. 31.
    Tufte, E. R., Envisioining Information. Cheshire: Graphics Press, 1990.Google Scholar
  32. 32.
    Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P., Kleijnen, J., and Moher, D., The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. PLoS Med. 6(7), 2009.
  33. 33.
    Ammann, R., Vandelanotte, C., de Vries, H., and Mummery, W. K., Can a Website-Delivered Computer-Tailored Physical Activity Intervention Be Acceptable, Usable, and Effective for Older People? Health Educ. Behav. 40(2):160–170, 2013. Scholar
  34. 34.
    Alley, S., Jennings, C., Plotnikoff, R. C., and Vandelanotte, C., My Activity Coach - Using video-coaching to assist a web-based computer-tailored physical activity intervention: a randomised controlled trial protocol. BMC Public Health 14, 2014.
  35. 35.
    Spittaels, H., De Bourdeaudhuij, I., Brug, J., and Vandelanotte, C., Effectiveness of an online computer-tailored physical activity intervention in a real-life setting. Health Educ. Res. 22(3):385–396, 2007. Scholar
  36. 36.
    Kelders, S. M., van Gemert-Pijnen, J. E., Werkman, A., and Seydel, E. R., Evaluation of a web-based lifestyle coach designed to maintain a healthy bodyweight. J. Telemed. Telecare 16(1):3–7, 2010. Scholar
  37. 37.
    King, A. C., Bickmore, T. W., Campero, M. I., Pruitt, L. A., and Yin, J. L., Employing Virtual Advisors in Preventive Care for Underserved Communities: Results From the COMPASS Study. J. Health Commun. 18(12):1449–1464, 2013. Scholar
  38. 38.
    Winett, R. A., Anderson, E. S., Wojcik, J. R., Winett, S. G., and Bowden, T., Guide to Health: Nutrition and Physical Activity Outcomes of a Group-Randomized Trial of an Internet-Based Intervention in Churches. Ann. Behav. Med. 33(3):251–261, 2007. Scholar
  39. 39.
    Antypas, K., and Wangberg, S. C., An Internet- and Mobile-Based Tailored Intervention to Enhance Maintenance of Physical Activity After Cardiac Rehabilitation: Short-Term Results of a Randomized Controlled Trial. J. Med. Internet Res. 16(3):78–95, 2014. Scholar
  40. 40.
    Morgan, P. J., Callister, R., Collins, C. E., Plotnikoff, R. C., Young, M. D., Berry, N., McElduff, P., Burrows, T., Aguiar, E., and Saunders, K. L., The SHED-IT Community Trial: A Randomized Controlled Trial of Internet- and Paper-Based Weight Loss Programs Tailored for Overweight and Obese Men. Ann. Behav. Med. 45(2):139–152, 2013. Scholar
  41. 41.
    Klausen, S. H., Mikkelsen, U. R., Hirth, A., Wetterslev, J., Kjaergaard, H., Sondergaard, L., and Andersen, L. L., Design and rationale for the PREVAIL study: Effect of e-Health individually tailored encouragements to physical exercise on aerobic fitness among adolescents with congenital heart disease—a randomized clinical trial. Am. Heart J. 163(4):549–556, 2012. Scholar
  42. 42.
    Hearn, L., Miller, M., and Fletcher, A., Online healthy lifestyle support in the perinatal period: what do women want and do they use it? Aust J Primary Health 19(4):313–318, 2013. Scholar
  43. 43.
    Brindal, E., Freyne, J., Saunders, I., Berkovsky, S., Smith, G., and Noakes, M., Features Predicting Weight Loss in Overweight or Obese Participants in a Web-Based Intervention: Randomized Trial. J. Med. Internet Res. 14(6):114–129, 2012. Scholar
  44. 44.
    Ezendam, N., Noordegraaf, V., Kroeze, W., Brug, J., and Oenema, A., Process evaluation of FATaintPHAT, a computer-tailored intervention to prevent excessive weight gain among Dutch adolescents. Health Promot. Int. 28(1):26–35, 2013. Scholar
  45. 45.
    Mitchell, B. L., Lewis, N. R., Smith, A. E., Rowlands, A. V., Parfitt, G., and Dollman, J., Rural Environments and Community Health (REACH): a randomised controlled trial protocol for an online walking intervention in rural adults. BMC Public Health 14, 2014.
  46. 46.
    Hurling, R., Catt, M., De Boni, M., Fairley, B. W., Hurst, T., Murray, P., Richardson, A., and Sodhi, J. S., Using Internet and Mobile Phone Technology to Deliver an Automated Physical Activity Program: Randomized Controlled Trial. J. Med. Internet Res. 9(2):1–12, 2007. Scholar
  47. 47.
    Rothert, K., Strecher, V. J., Doyle, L. A., Caplan, W. M., Joyce, J. S., Jimison, H. B., Karm, L. M., Mims, A. D., and Roth, M. A., Web-based Weight Management Programs in an Integrated Health Care Setting: A Randomized, Controlled Trial. Obes 14(2):266–272, 2006. Scholar
  48. 48.
    Patrick, K., Marshall, S. J., Davila, E. P., Kolodziejczyk, J. K., Fowler, J. H., Calfas, K. J., Huang, J. S., Rock, C. L., Griswold, W. G., Gupta, A., Merchant, G., Norman, G. J., Raab, F., Donohue, M. C., Fogg, B. J., and Robinson, T. N., Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). Contemp Clin Trials 37(1):10–18, 2014. Scholar
  49. 49.
    Funk, K. L., Stevens, V. J., Bauck, A., Brantley, P. J., Hornbrook, M., Jerome, G. J., Myers, V. H., and Appel, L., Development and Implementation of a Tailored Self-assessment Tool in an Internet-based Weight Loss Maintenance Program. Clin. Pract. Epidemiol. Ment. Health 7:67–73, 2011. Scholar
  50. 50.
    Parekh, S., King, D., Boyle, F. M., and Vandelanotte, C., Randomized controlled trial of a computer-tailored multiple health behaviour intervention in general practice: 12-month follow-up results. Int. J. Behav. Nutr. Phys. Act. 11, 2014.
  51. 51.
    Martinez, J. L., Duncan, L. R., Rivers, S. E., Latimer, A. E., and Salovey, P., Examining the use of message tailoring to promote physical activity among medically underserved adults. J. Health Psychol. 18(4):470–476, 2013. Scholar
  52. 52.
    Cheetham, A. H., and Hazel, J. E., Binary (Presence-Absence) Similarity Coefficients. J. Paleontol. 43(5):1130–1136, 1969. Scholar
  53. 53.
    Gower, J. C., A General Coefficient of Similarity and Some of Its Properties. Biometrics 27(4):857–871, 1971. Scholar
  54. 54.
    Oinas-Kukkonen, H., and Harjumaa, M., Persuasive Systems Design: Key Issues, Process Model, and System Features. Comm Assoc Inform Syst 24:485–500, 2009.Google Scholar
  55. 55.
    Fisher, C. M., Adapting the Information–Motivation–Behavioral Skills Model:Predicting HIV-Related Sexual Risk Among Sexual Minority Youth. Health Educ. Behav. 39(3):290–302, 2012. Scholar
  56. 56.
    Ajzen, I., and Fishbein, M., Understanding attitudes and predicting social behaviour. Englewood Cliffs: Prentice-Hall, 1980.Google Scholar
  57. 57.
    Win, K. T., Hassan, N., Bonney, A., and Iverson, D., Benefits of Online Health Education: Perception from Consumers and Health Professionals. J. Med. Syst. 39(3):1–8, 2015. Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of WollongongWollongongAustralia
  2. 2.University of Illinois - ChicagoChicagoUSA
  3. 3.Texas A&M International UniversityLaredoUSA

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