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An Agent-Based Model of Healthy Eating with Applications to Hypertension

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Advanced Data Analytics in Health

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 93))

Abstract

Changing descriptive social norm in health behavior (“how many people are behaving healthy”) has been shown to be effective in promoting healthy eating. We developed an agent-based model to explore the potential of changing social norm in reducing hypertension among the adult population of Los Angeles County. The model uses the 2007 California Health Interview Survey (CHIS) to create a virtual population that mimics the joint distribution of demographic characteristics and health behavior in the Los Angeles County. We calibrated the outcome of hypertension as a function of individual age and fruits/vegetable consumption, based upon the observed pattern in the survey. We then simulated an intervention scenario to promote healthier eating by increasing the visibility (i.e. descriptive social norms) of those who eat at least one serving of fruits/vegetable per day. We compare the hypertension incidence under the status quo scenario and the intervention scenario. We found that the effect size of 5% in social norm enhancement yields a reduction in 5 year hypertension incidence by 10.08%. An effect size of 15% would reduce incidence by 15.50%. In conclusion, the agent-based model built and calibrated around real-world data shows that changes descriptive social norms in healthy eating can be effective to reduce the burden of hypertension. The model can be improved in the future by also including the chronic conditions that are affected by changes in fruits/vegetable consumption.

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Acknowledgements

PJG wishes to thank the College of Liberal Arts & Sciences and the Department of Computer Science at Northern Illinois University for financial support.

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Authors and Affiliations

  1. Department of Industrial Engineering, Clemson University, Clemson, SC, 29634, USA

    Amin Khademi & Chengqian Luo

  2. Department of Health Policy and Management, University of Georgia, Athens, GA, 30602, USA

    Donglan Zhang

  3. Computer Science Department, Furman University, Greenville, SC, 29613, USA

    Philippe J. Giabbanelli

  4. School of Nursing, Clemson University, Clemson, SC, 29634, USA

    Shirley Timmons

  5. Department of Public Health Sciences, Clemson University, Clemson, SC, 29634, USA

    Lu Shi

Authors
  1. Amin Khademi
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  2. Donglan Zhang
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  3. Philippe J. Giabbanelli
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  4. Shirley Timmons
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  5. Chengqian Luo
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  6. Lu Shi
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Corresponding author

Correspondence to Philippe J. Giabbanelli .

Editor information

Editors and Affiliations

  1. Computer Science Department, Furman University, Greenville, South Carolina, USA

    Philippe J. Giabbanelli

  2. Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada

    Vijay K. Mago

  3. Department of Computer Engineering, Technological Educational Institute, Lamia, Greece

    Elpiniki I. Papageorgiou

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Khademi, A., Zhang, D., Giabbanelli, P.J., Timmons, S., Luo, C., Shi, L. (2018). An Agent-Based Model of Healthy Eating with Applications to Hypertension. In: Giabbanelli, P., Mago, V., Papageorgiou, E. (eds) Advanced Data Analytics in Health. Smart Innovation, Systems and Technologies, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-77911-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-77911-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77910-2

  • Online ISBN: 978-3-319-77911-9

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