Activating a Community: An Agent-Based Model of Romp & Chomp, a Whole-of-Community Childhood Obesity Intervention
Activating a Community: An Agent-Based Model of Romp & Chomp, a Whole-of-Community Childhood Obesity Intervention
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DOI：
10.1002/oby.22553
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发表时间：
2019-07-25
期刊：
影响因子：
6.9
通讯作者：
Nichols, Melanie
中科院分区：
文献类型：
--
作者：
Kasman, Matt;Hammond, Ross A.;Nichols, Melanie
目的成功的全社区儿童肥胖预防干预措施往往涉及社区利益相关者在传播知识和参与肥胖预防工作通过社区。这一过程被作者称为业主驱动的社区扩散（SDCD）。本研究使用基于代理的模型结合干预数据，以增加对SDCD如何运作的理解。方法该基于代理的模型回顾性模拟了澳大利亚维多利亚为期4年的全社区儿童肥胖预防干预活动Romp & Chomp期间的SDCD。利益相关者的调查数据，干预记录，和专家的估计被用来参数化的模型。根据经验数据和专家对社区知识和参与变化的程度和时间的估计得出的标准对模型输出进行了评估。结果该模型能够产生符合评估标准的输出：SDCD驱动的模拟社区知识和参与度的增加与专家估计的幅度和时间密切匹配。结论强有力的暗示性证据支持一个假设，即SDCD是Romp & Chomp干预成功的关键驱动力。模型探索还提供了关于这些过程的更多见解（包括在哪些方面额外的数据收集可能证明是最有益的），以及对未来干预措施的设计和实施的影响。
Objective Successful whole-of-community childhood obesity prevention interventions tend to involve community stakeholders in spreading knowledge about and engagement with obesity prevention efforts through the community. This process is referred to by the authors as stakeholder-driven community diffusion (SDCD). This study uses an agent-based model in conjunction with intervention data to increase understanding of how SDCD operates. Methods This agent-based model retrospectively simulated SDCD during Romp & Chomp, a 4-year whole-of-community childhood obesity prevention intervention in Victoria, Australia. Stakeholder survey data, intervention records, and expert estimates were used to parameterize the model. Model output was evaluated against criteria derived from empirical data and experts' estimates of the magnitude and timing of community knowledge and engagement change. Results The model was able to produce outputs that met the evaluation criteria: increases in simulated community knowledge and engagement driven by SDCD closely matched expert estimates of magnitude and timing. Conclusions Strong suggestive evidence was found in support of a hypothesis that SDCD was a key driver of the success of the Romp & Chomp intervention. Model exploration also provided additional insights about these processes (including where additional data collection might prove most beneficial), as well as implications for the design and implementation of future interventions.