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Combating Obesity through Healthy Eating Behavior: A Call for System Dynamics Optimization

Author

Listed:
  • Norhaslinda Zainal Abidin
  • Mustafa Mamat
  • Brian Dangerfield
  • Jafri Haji Zulkepli
  • Md Azizul Baten
  • Antoni Wibowo

Abstract

Poor eating behavior has been identified as one of the core contributory factors of the childhood obesity epidemic. The consequences of obesity on numerous aspects of life are thoroughly explored in the existing literature. For instance, evidence shows that obesity is linked to incidences of diseases such as heart disease, type-2 diabetes, and some cancers, as well as psychosocial problems. To respond to the increasing trends in the UK, in 2008 the government set a target to reverse the prevalence of obesity (POB) back to 2000 levels by 2020. This paper will outline the application of system dynamics (SD) optimization to simulate the effect of changes in the eating behavior of British children (aged 2 to 15 years) on weight and obesity. This study also will identify how long it will take to achieve the government’s target. This paper proposed a simulation model called Intervention Childhood Obesity Dynamics (ICOD) by focusing the interrelations between various strands of knowledge in one complex human weight regulation system. The model offers distinct insights into the dynamics by capturing the complex interdependencies from the causal loop and feedback structure, with the intention to better understand how eating behaviors influence children’s weight, body mass index (BMI), and POB measurement. This study proposed a set of equations that are revised from the original (baseline) equations. The new functions are constructed using a RAMP function of linear decrement in portion size and number of meal variables from 2013 until 2020 in order to achieve the 2020 desired target. Findings from the optimization analysis revealed that the 2020 target won’t be achieved until 2026 at the earliest, six years late. Thus, the model suggested that a longer period may be needed to significantly reduce obesity in this population.

Suggested Citation

  • Norhaslinda Zainal Abidin & Mustafa Mamat & Brian Dangerfield & Jafri Haji Zulkepli & Md Azizul Baten & Antoni Wibowo, 2014. "Combating Obesity through Healthy Eating Behavior: A Call for System Dynamics Optimization," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-17, December.
  • Handle: RePEc:plo:pone00:0114135
    DOI: 10.1371/journal.pone.0114135
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    Cited by:

    1. Jose D Meisel & Olga L Sarmiento & Camilo Olaya & Pablo D Lemoine & Juan A Valdivia & Roberto Zarama, 2018. "Towards a novel model for studying the nutritional stage dynamics of the Colombian population by age and socioeconomic status," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.
    2. Oleg V. Pavlov & Jason M. Sardell, 2023. "Economic Origins of the Sicilian Mafia: A Simulation Feedback Model," Papers 2304.07975, arXiv.org.
    3. Oleg V Pavlov & Evangelos Katsamakas, 2020. "Will colleges survive the storm of declining enrollments? A computational model," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-29, August.
    4. Chang, Angela Y. & Ogbuoji, Osondu & Atun, Rifat & Verguet, Stéphane, 2017. "Dynamic modeling approaches to characterize the functioning of health systems: A systematic review of the literature," Social Science & Medicine, Elsevier, vol. 194(C), pages 160-167.
    5. Farah Hanim Abdul Rahim* & NurulNazihahHawari & Norhaslinda Zainal Abidin, 2018. "The Impact of Price Subsidy on Rice Self-Sufficiency Level in Malaysia: A System Dynamics Approach," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 801-806:6.
    6. Fontoura, Wlisses Bonelá & Chaves, Gisele de Lorena Diniz & Ribeiro, Glaydston Mattos, 2019. "The Brazilian urban mobility policy: The impact in São Paulo transport system using system dynamics," Transport Policy, Elsevier, vol. 73(C), pages 51-61.
    7. Deng, Yewen & Li, Na & Jiang, Zhibin & Xie, Xiaoqing & Kong, Nan, 2021. "Optimal differential subsidy policy design for a workload-imbalanced outpatient care network," Omega, Elsevier, vol. 99(C).

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