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Health and Habit: An Agent-based Approach

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KI 2022: Advances in Artificial Intelligence (KI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13404))

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Abstract

Data-driven models with weak theoretical foundations for the examination of interventions and concepts to improve and maintain health lack explainability of results and suggestions. The use of agent-based models is a possible approach to remedy this issue. Modelling behaviour and the formation of habits using established theoretical psychological frameworks is a way of improving the utilisation of agent-based models when researching health-related questions. This paper proposes a concept implementing the Health Action Process Approach and the Social Cognitive Learning Theory to model the process of behaviour change within the Beliefs-Desires-Intentions Framework. The concept illustrates how an agent workflow can incorporate these psychological models and explain how social influence contributes to the formation of habits.

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References

  1. Chiagunye, T.: Markov chain approach to agent based modelling (ABM) of an industrial machine operation control. J. Multi. Eng. Sci. Technol. 2, 4 (2015)

    Google Scholar 

  2. Lorig, F., et al.: Agent-based social simulation of the covid-19 pandemic: a systematic review. J. Artif. Soc. Soc. Simul. 24(3), 5 (2021)

    Article  Google Scholar 

  3. Timm, I., Spaderna, H., Rodermund, S., Lohr, C., Buettner, R., Berndt, J.O.: Designing a randomized trial with an age simulation suit-representing people with health impairments. Healthcare 9, 27 (2021)

    Article  Google Scholar 

  4. Fife-Schaw, C., et al.: Simulating behaviour change interventions based on the theory of planned behaviour: impacts on intention and action. Br. J. Soc. Psychol. 46(04), 43–68 (2007)

    Article  Google Scholar 

  5. Cattadori, G., et al.: Exercise and heart failure: an update: exercise and heart failure. ESC Heart Fail. 5, 12 (2017)

    Google Scholar 

  6. Mollee, J.S., van der Wal, C.N.: A computational agent model of influences on physical activity based on the social cognitive theory. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS (LNAI), vol. 8291, pp. 478–485. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44927-7_37

    Chapter  Google Scholar 

  7. Villanti, A.C., et al.: Smoking-cessation interventions for U.S. young adults: updated systematic review. Am. J. Prev. Med. 59(1), 123–136 (2020)

    Google Scholar 

  8. Zhang, D., et al.: Impact of different policies on unhealthy dietary behaviors in an urban adult population: an agent-based simulation model. Am. J. Public Health 104, 05 (2014)

    Article  Google Scholar 

  9. Baldensperger, L., et al.: Physical activity among adults with obesity: testing the health action process approach. Rehabil. Psychol. 59, 01 (2014)

    Article  Google Scholar 

  10. Beheshti, R., et al.: Comparing methods of targeting obesity interventions in populations: an agent-based simulation. SSM - Popul. Health 3, 211–218 (2017)

    Article  Google Scholar 

  11. Giabbanelli, P., et al.: Modeling the influence of social networks and environment on energy balance and obesity. J. Comput. Sci. 3(03), 17–27 (2012)

    Article  Google Scholar 

  12. Allan, V., et al.: The use of behaviour change theories and techniques in research-informed coach development programmes: a systematic review. Int. Rev. Sport Exerc. Psychol. 11(1), 47–69 (2018)

    Article  Google Scholar 

  13. Schwarzer, R., et al.: Mechanisms of health behavior change in persons with chronic illness or disability: the health action process approach (HAPA). Rehabil. Psychol. 56(08), 161–70 (2011)

    Article  Google Scholar 

  14. Rao, A., George, M.: BDI agents: from theory to practice. In: Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp. 312–319 (1995)

    Google Scholar 

  15. Bandura, A.: Lernen am Modell: Ansatze zu einer sozial-kognitiven Lerntheorie. Klett (1976)

    Google Scholar 

  16. Smith, S., et al.: Social-cognitive predictors of parental supervised toothbrushing: an application of the health action process approach. Br. J. Health Psychol. 26 (2021). https://doi.org/10.1111/bjhp.12516

  17. Wilson, H., et al.: Self-efficacy, planning, and drink driving: applying the health action process approach. Health Psychol. 35 (2016). https://doi.org/10.1037/hea0000358

  18. Pourhaji, F., et al.: Application of the health action process approach model in predicting mammography among Iranian women (2020). https://doi.org/10.21203/rs.3.rs-80108/v1

  19. Castelfranchi, C., Werner, E. (eds.): MAAMAW 1992. LNCS, vol. 830. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58266-5

    Book  MATH  Google Scholar 

  20. Bandura, A.: The power of observational learning through social modeling. In: Stenberg, R., Fiske, S.T., Foss, D.J. (eds.). Scientists Making a Difference, pp. 235–239 (2016)

    Google Scholar 

  21. Andsager, J., et al.: Perceived similarity of exemplar traits and behavior effects on message evaluation. Commun. Res. 33, 3–18 (2006)

    Article  Google Scholar 

  22. Verplanken, B., Aarts, H.: Habit, attitude, and PlannedBehaviour: is habit an empty construct or an interesting case of goal-directed automaticity? Eur. Revi. Soc. Psychol. 10(1), 101–134 (1999)

    Article  Google Scholar 

  23. James, W.: The Principles of Psychology, vol. 1, Macmillan, London (1890)

    Google Scholar 

  24. Aarts, H., et al.: Predicting behavior from actions in the past: repeated decision making or a matter of habit? J. Appl. Soc. Psychol. 28, 1355–1374 (1998)

    Article  Google Scholar 

  25. Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist. Open University Press, USA (2005)

    Google Scholar 

  26. Cardoso, R.C., Ferrando, A.: A review of agent-based programming for multi-agent systems. Computers 10(2), 16 (2021)

    Article  Google Scholar 

  27. Weiss, G.: Multiagent Systems. The MIT Press, Cambridge (2013)

    Google Scholar 

  28. Jager, W.: Enhancing the realism of simulation (EROS): on implementing and developing psychological theory in social simulation. J. Artif. Soc. Soc. Simul. 20, 14 (2017)

    Article  Google Scholar 

  29. Mercuur, R., et al.: Integrating social practice theory in agent-based models: a review of theories and agents. IEEE Trans. Comput. Soc. Syst. 7(5), 1131–1145 (2020)

    Article  Google Scholar 

  30. Taghikhah, F., et al.: Where does theory have it right? A comparison of theory-driven and empirical agent based models. J. Artif. Soc. Soc. Simul. 24, 4 (2020)

    Article  Google Scholar 

  31. Mozahem, N.: Social cognitive theory and women’s career choices: an agent-based model simulation. Comput. Math. Organ. Theor. 10, 2020 (2020)

    Google Scholar 

  32. Wu, C., et al.: Emergence of informal safety leadership: a social-cognitive process for accident prevention. Prod. Oper. Manag. 30(11), 4288–4305 (2021)

    Article  Google Scholar 

  33. Berndt, J.O., Rodermund, S., Timm, I.J.: Social contagion of fertility: an agent-based simulation study. In: Winter Simulation Conference, pp. 953–964 (2018)

    Google Scholar 

  34. Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds.): PRIMA 2018. LNCS (LNAI), vol. 11224. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03098-8

    Book  Google Scholar 

  35. Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds.): CISIS 2018. AISC, vol. 772. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93659-8

    Book  Google Scholar 

  36. Sugiarto, V., et al.: Modeling agent-oriented methodologies for landslide management. J. Inf. Technol. Comput. Sci. 4(2), 193–201 (2019)

    Google Scholar 

  37. Adam, C.: Emotions: from psychological theories to logical formalization and implementation in a BDI agent, July 2007

    Google Scholar 

  38. Courneya, K.S., Hellsten, L.-A.M.: Personality correlates of exercise behavior, motives, barriers and preferences: an application of the five-factor model. Pers. Individ. Differ. 24(5), 625–633 (1998)

    Article  Google Scholar 

  39. Piepoli, M., et al.: Experience from controlled trials of physical training in chronic heart failure. Protocol and patient factors in effectiveness in the improvement in exercise tolerance. Eur. Heart J. 19(3), 466–475 (1998)

    Google Scholar 

  40. James, J., Annesi, P.D.: Relationship of perceived health and appearance improvement, and self-motivation, with adherence to exercise in previously sedentary women. Eur. J. Sport Sci. 4(2), 1–13 (2004)

    Google Scholar 

  41. Singer, P.: Utilitarianism and vegetarianism. Filosofia Unisinos. 17, (2016). https://doi.org/10.4013/fsu.2016.172.17

  42. Coomber, K., et al.: Awareness and correlates of short-term and long-term consequences of alcohol use among Australian drinkers. Aust. NZ J. Public Health. 41(3), 237–242 (2017)

    Article  Google Scholar 

  43. Dalum, P., et al.: A cluster randomised controlled trial of an adolescent smoking cessation intervention: short and long-term effects. Scand. J. Public Health 40(2), 167–76 (2012)

    Article  Google Scholar 

  44. Keadle, S., et al.: Prevalence and trends in physical activity among older adults in the United States: a comparison across three national surveys. Prev. Med. 89, 05 (2016)

    Article  Google Scholar 

  45. Kaaronen, R.O., Stelkovsii, N.: Cultural evolution of sustainable behaviors: pro-environmental tipping points in an agent-based model. One Earth 2(1), 85–97 (2020)

    Article  Google Scholar 

  46. Klein, M., et al.: Contagion of habitual behaviour in social networks: an agent-based model. In: International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, pp. 538–545 (2012)

    Google Scholar 

  47. Jensen, T., et al.: Agent-based assessment framework for behavior-changing feedback devices: spreading of devices and heating behavior. Technol. Forecast. Soc. Change 98, 105–119 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Veronika Kurchyna, Jan Ole Berndt & Ingo J. Timm

  2. Universität Trier, 54293, Trier, Germany

    Veronika Kurchyna, Stephanie Rodermund, Heike Spaderna & Ingo J. Timm

Authors
  1. Veronika Kurchyna
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  2. Stephanie Rodermund
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  3. Jan Ole Berndt
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  4. Heike Spaderna
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  5. Ingo J. Timm
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Corresponding author

Correspondence to Veronika Kurchyna .

Editor information

Editors and Affiliations

  1. University of Trier, Trier, Rheinland-Pfalz, Germany

    Ralph Bergmann

  2. University of Trier, Trier, Germany

    Lukas Malburg

  3. University of Trier, Trier, Germany

    Stephanie C. Rodermund

  4. University of Trier, Trier, Germany

    Ingo J. Timm

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Kurchyna, V., Rodermund, S., Berndt, J.O., Spaderna, H., Timm, I.J. (2022). Health and Habit: An Agent-based Approach. In: Bergmann, R., Malburg, L., Rodermund, S.C., Timm, I.J. (eds) KI 2022: Advances in Artificial Intelligence. KI 2022. Lecture Notes in Computer Science(), vol 13404. Springer, Cham. https://doi.org/10.1007/978-3-031-15791-2_12

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  • DOI: https://doi.org/10.1007/978-3-031-15791-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15790-5

  • Online ISBN: 978-3-031-15791-2

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