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Modeling Emotion and Behavior in Animated Personas to Facilitate Human Behavior Change: The Case of the HEART-SENSE Game

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Abstract

The goal of this research is to determine whether a computer based training game (HEART-SENSE) can improve recognition of heart attack symptoms and shift behavioral issues so as to reduce pre-hospitalization delay in seeking treatment. Since treatment delay correlates with adverse outcomes, this research could reduce myocardial infarction mortality and morbidity. In Phase I we created and evaluated a prototype virtual village in which users encounter and help convince synthetic personas to deal appropriately with a variety of heart attack scenarios and delay issues. Innovations made here are: (1) a design for a generic simulator package for promoting health behavior shifts, and (2) algorithms for animated pedagogical agents to reason about how their emotional state ties to patient condition and user progress. Initial results show that users of the game exhibit a significant shift in intention to call 9-1-1 and avoid delay, that multi-media versions of the game foster vividness and memory retention as well as a better understanding of both symptoms and of the need to manage time during a heart attack event. Also, results provide insight into areas where emotive pedagogical agents help and hinder user performance. Finally, we conclude with next steps that will help improve the game and the field of pedagogical agents and tools for simulated worlds for healthcare education and promotion.

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References

  1. J.C. Lester, S.A. Converse, S.E. Kahler et al., The persona effect: affective impact of animated pedagogical agents, CHI 97 (22–27 March 1997) 359–366.

  2. M.S. El-Nasr, T.R. Ioeger and J. Yen, A Web of emotions, in: Emotion Based Agent Architecture Workshop at Autonomous Agents'99 Conference, ed. J. Velasquez, www.ai.mit.edu/people/jvelas/ebaa99.

  3. J. Gratch, Why you should buy an emotional planner, in: Emotion Based Agent Architecture Workshop at Autonomous Agents'99 Conference, ed. J. Velasquez, www.ai.mit.edu/people/jvelas/ebaa99.

  4. J.D. Velásquez, From affect programs to higher cognitive emotions: an emotion-based control approach, in: Emotion Based Agent Architecture Workshop at Autonomous Agents'99 Conference, ed. J. Velasquez, www.ai.mit.edu/people/jvelas/ebaa99.

  5. R.A. Carleton et al., Report of the Expert Panel on Awareness and Behavior Change to the Board of Directors, American Heart Association (1996); www.americanheart.org/Scientific/statements/1996/059601.html.

  6. J. Reis and F. Wrestler, Consumer attitudes towards computer-assisted self-care of the common cold, Patient Education and Counseling 23 (1994) 55–62.

    Google Scholar 

  7. S. Foster and M. Mallik, A comparative study of differences, in: The Referral Behaviour Patterns of Men and Women who Have Experienced Cardiac-Related Chest Pain (1998) pp. 192–202.

  8. N. Bett, G. Aroney and P. Thompson, Impact of a national educational campaign to reduce patient delay in possible heart attack, Aust. NZ J. Med. 23 (1993) 157–161.

    Google Scholar 

  9. D.C. Goff, Jr. et al., Knowledge of heart attack symptoms in a population survey in the United States, Arch. Intern. Med. 158 (1998) 2329–2338.

    Google Scholar 

  10. Symposium Proceedings, New Information Technology and the National Heart Attack Alert Program: Setting a 5-Year Agenda, Lister Hill Center, National Library of Medicine, Bethesda, MD (14–15 April 1998).

  11. M. Fishbein, The role of theory in HIV prevention, Philadelphia, Public Policy Center, Annenberg School for Communication, University of Pennsylvania (2000), unpublished work.

    Google Scholar 

  12. B. Silverman, E. Cobbs, P. Pincetl, C. Motta and R.-L. Liao, Toward an interactive learning environment for conveying biopsyhosocialvalues skills to clinicians, GWU/IAI Tech. report (1992).

  13. D.M. Dehn and S. Mulken, The impact of animated interface agents: a review of empirical research, Int. J. Human-Computer Studies 52 (2000) 1–22.

    Google Scholar 

  14. F. Rhodes, M. Fishbein and J. Reis, Using behavioral theory in computer-based health promotion and appraisal, Health Education & Behavior 24(1) (1997) 20–34.

    Google Scholar 

  15. P.C. Hardin and J. Reis, Interactive multimedia software design: concepts, process, and evaluation, Health Education & Behavior 24(1) (1997) 35–53.

    Google Scholar 

  16. K. Dracup, D.K. Moser, M. Eisenberg et al., Causes of delay in seeking treatment for heart attack symptoms, Soc. Sci. Med. 40(3) (1995) 379–392.

    Google Scholar 

  17. D. Lewis, Computer-based approaches to patient education: a review of the literature, JAMIA 6 (1999) 272–282.

    Google Scholar 

  18. J. Lasseter, Principles of traditional animation applied to computer animation, Computer Graphics 21(4) (July 1987) 35–44.

    Google Scholar 

  19. [19] Anonimous, Microsoft Agent Development Kit (Microsoft Press, Redmond, 1999).

    Google Scholar 

  20. N.E. Avis, K.W. Smith and J.B. McKinlay, Accuracy of perceptions of heart attack risk: what influences perceptions and can they be changed? American Journal of Public Health 79 (1989) 1608–1612.

    Google Scholar 

  21. T.W. Bickmore, L.K. Cook, E.F. Churchill et al., Animated autonomous personal representatives, in: Proc. 2nd Int. Conf. on Automnomous Agents (ACM Press, New York, May 1999) pp. 8–15.

    Google Scholar 

  22. J.K. Bleeker, L.M. Lamers, I.M. Leenders et al., Psychological and knowledge factors related to delay of help-seeking by patients with acute myocardial infarction, Psychotherapy & Psychosomatics 63 (1995) 151–158.

    Google Scholar 

  23. N. Chaturvedi, H. Rai and Y. Ben-Shlomo, Lay diagnosis and healthcare-seeking behaviour for chest pain in south Asians and Europeans, Lancet 350 (1997) 1578–1583.

    Google Scholar 

  24. L.T. Clark, S.V. Bellam, A.H. Shah and J.G. Feldman, Analysis of prehospital delay among inner-city patients with symptoms of myocardial infarction: implications for therapeutic intervention, Journal of the National Medical Association 84 (1992) 931–937.

    Google Scholar 

  25. R. Cundick, C.W. Turner et al., ILIAD as a patient case simulator to teach medical problem solving, in: SCAMC Conf. Proc. (1989) pp. 902–906.

  26. K. Dracup and D.K. Moser, Treatment-seeking behavior among those with signs and symptoms of acute myocardial infarction, Heart & Lung 20 (1991) 1–5.

    Google Scholar 

  27. K. Dracup, S.M. McKinley and D.K. Moser, Australian patients’ delay in response to heart attack symptoms,Medical Journal of Australia 166 (1997) 233–236.

    Google Scholar 

  28. C. Elliot, K. Williams and B.P. Woolf, An intelligent learning environment for advanced cardiac life support, in: Proc. AMIA Conf. (AMIA, Bethesda, 1996).

    Google Scholar 

  29. L.G. Esteve, M. Valdes, N. Riesco, I. Jodar and T. de Flores, Denial mechanisms in myocardial infarction: their relations with psychological variables and short-term outcome, Journal of Psychosomatic Research 36 (1992) 491–496.

    Google Scholar 

  30. K.B. Fields,Myocardial infarction and denial, Journal of Family Practice 28 (1989) 157–161.

  31. S.P. Lajoie and S.J. Derry, Computers as Cognitive Tools (Erlbaum, Hillsdale, 1993).

    Google Scholar 

  32. M. Magazine, Remarks in his keynote address, in: 2nd Int. Conf. on Automnomous Agents (ACM Press, New York, May 1999) (also see www.vpersons.com).

    Google Scholar 

  33. P. Pincetl, G.O. Barnett and E.P. Hoffer, Chest Pain: An Exercise in Clinical Problem Solving, RxDx Software Series (Williams and Wilkins, New York, 1987).

    Google Scholar 

  34. J. Psotka, L.D. Massey and S.A. Mutter, eds., Intelligent Tutoring Systems – Lessons Learned (Erlbaum, Hillsdale, 1988).

    Google Scholar 

  35. B. Reeves and C. Nass, The Media Equation: How People Treat Computers, Television, and NewMedia Like Real People and Places (CSLI Publications, Stanford, 1998).

    Google Scholar 

  36. J. Russo, From pixels to personality: digital characters are virtually human, Investors Business Daily (21 July 1998).

  37. B.G. Silverman, Human-computer collaboration, Human-Computer Interaction 7(2) (Summer 1992a).

  38. [38] B.G. Silverman, Critiquing Human Error: A Knowledge Based Human-Computer Collaborative Approach (Academic Press, London, 1992b).

    Google Scholar 

  39. B.G. Silverman, E. Cobbs, P. Pincetl et al., An interactive learning environment for health care professionals, in: Proc. 18th SCAMC, American Medical Informatics Assoc. (October 1994) pp. 49–53.

  40. B.G. Silverman, Computer Supported Collaborative Learning (CSCL), Computers in Education Jnl. 25(3) (December 1995) 81–91.

    Google Scholar 

  41. B.G. Silverman, The role of Web agents in medical knowledge management, MD Computing 15(4) (July 1998) 221–231.

    Google Scholar 

  42. E. Solloway, ed., Technology in education: special issue, CACM 36(5) (May 1993).

  43. E. Wener, Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge (Morgan Kaufman, Mountain View, 1987).

    Google Scholar 

  44. M. Fishbein, A. Bandura, H.C. Triandis, F.H. Kanfer, M.H. Becker and S.E. Middlestadt, Factors Influencing Behavior and Behavior Change: Final Report – Theorist's Workshop (National Institute of Mental Health, Rockville, MD, 1992).

    Google Scholar 

  45. M. Becker, The health belief model and personal health behavior, Health Education Monographs 2 (1974) 324–473.

    Google Scholar 

  46. I.M. Rosenstock, V.J. Strecher and M.J. Becker, The health belief model and HIV risk behavior change, in: Preventing AIDS: Theories and Methods of Behavioral Interventions, eds. R.J. Diclemente and J.L. Peterson (Plenum, New York, 1994) pp. 5–24.

    Google Scholar 

  47. A. Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory (Prentice Hall, Englewood Cliffs, NJ, 1986).

    Google Scholar 

  48. A. Bandura, Social cognitive theory and exercise of control over HIV infection, in: Preventing AIDS: Theories and Methods of Behavioral Interventions, eds. R.J. Diclemente and J.L. Peterson (Plenum, New York, 1994) pp. 25–60.

  49. I. Ajzen and M. Fishbein, Understanding Attitudes and Predicting Social Behavior (Prentice Hall, Englewood Cliffs, NJ) p. 198.

  50. Pennsylvania State Data Center, Pennsylvania Fact Sheet, Penn State Harrisburg Institute for State and Regional Affairs (2000) 1–2.

  51. S. Norman, Women's Contraceptive and Reproductive Experiences (CARE) Study (3–2000).

  52. A. Isen, Positive affect and decision making, in: Handbook of Emotions, eds. M. Lewis and J. Haviland (Guilford Press, New York, 1983) ch. 19, pp. 261–277.

    Google Scholar 

  53. J. Lasseter, Principles of traditional animation applied to computer animation, Computer Graphics 21(4) (July 1987) 35–44.

    Google Scholar 

  54. Anonimous, Microsoft Agent Development Kit (Microsoft Press, Redmond, 1999).

    Google Scholar 

  55. A. Ortony, G.L. Clore and A. Collins, The Cognitive Structure of Emotions (Cambridge University Press, Cambridge, 1988).

    Google Scholar 

  56. P. Wright, R. Milroy and A. Lickorish, Static and animated graphics in learning from interactive texts, European Journal of Psychology of Education (Special Issue on Visual Learning with New Technologies) (1999).

  57. S.J. Brown, D.A. Lieberman, B.A. Germeny, Y.C. Fan, D.M. Wilson and D.J. Pasta, Educational video game for juvenile diabetes: results of a controlled trial, Medical Informatics 22(1) (1997) 77–89.

    Google Scholar 

  58. P.P. Horan, M.C. Tarborough, G. Besigel and D.R. Carlson, Computer-assisted self control of diabetes by adolescents, Diabetes Education 16(3) (1990)205–211.

    Google Scholar 

  59. D.G. Marrero, K.K. Kronz, M.P. Golden, J.C. Wright, D.P. Orr and N.S. Fineberg, Clinical evaluation of computer-assisted selfmonitoring of blood glucose system, Diabetes Care 12(5) (1989) 345–350.

    Google Scholar 

  60. C. Meyerhoff, F. Bischof and E.F. Pfeiffer, Long-term experience with a computerized diabetes management and glucose monitoring system in insulin-dependent diabetic patients, Diabetes Research in Clinical Practice 24(1) (1994) 1–7.

    Google Scholar 

  61. L. Liao, J.G. Jollis, E.R. DeLong, E.D. Peterson, K.G. Morris and D.B. Mark, Impact of an interactive video on decision making of patients with ischemic heart disease, Journal of General Internal Medicine 11(6) (1996) 373–376.

    Google Scholar 

  62. L.M. Osman, M.I. Abdalla, J.A. Beattie et al., Reducing hospital admission through computer supported education for asthma patients: Grampian asthma study of integrated care (GRASSIC), British Medical Journal 308(6928) (1994) 568–571.

    Google Scholar 

  63. D.H. Rubin, J.M. Leventhal, R.T. Sadock et al., Educational intervention by computer in childhood asthma: a randomized clinical trial testing the use of a new teaching intervention in childhood asthma, Pediatrics 77(1) (1986) 1–10.

    Google Scholar 

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Silverman, B.G., Holmes, J., Kimmel, S. et al. Modeling Emotion and Behavior in Animated Personas to Facilitate Human Behavior Change: The Case of the HEART-SENSE Game. Health Care Management Science 4, 213–228 (2001). https://doi.org/10.1023/A:1011448916375

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