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Using Simulations for Exploring Interventions in Social Networks: Modeling Physical Activity Behaviour in Dutch School Classes

机译:利用模拟探索社交网络中的干预:荷兰学校课程中的体育活动行为

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The reduction of childhood obesity through the promotion of a healthy lifestyle is one of the most important public health challenges at the moment. It is known that the unhealthy habits of children can cause unavoidable side effects in their early stage of life, including both physical and mental consequences. This work considers that the physical activity level of children is a behaviour that can be spread throughout the social relations of children in their daily life at school. Therefore, the aim of this work is to define what the best strategy is to find 'targets' (i.e., influential children that can initiate behavioural change) for physical activity (PA) interventions that would affect the average PA of a population of Dutch school classes. We tuned a model based on the influence of the children's peers in their social network, based on the data set from the MyMovez project - Phase I. Five intervention strategies were implemented, and their efficacy was compared. Once the targets were chosen, an increase of 17% was applied to their initial PA. Then, the diffusion model was run to verify the improvement on the PA of the whole network after one year. We discuss implications of the simulation results on which strategies may be used to make informed choices about the setup of social network interventions and future model improvements. Our results show that targeting more vulnerable children (i.e. in a worse environment) and applying a network optimization algorithm are the best solutions for this data set indicating that future interventions should aim for these two strategies.
机译:通过促进健康的生活方式减少儿童肥胖是目前最重要的公共卫生挑战之一。众所周知,儿童的不健康习惯可能会在其早期生命阶段引起不可避免的副作用,包括身体和心理后果。这项工作认为,儿童的身体活动水平是在学校日常生活中遍布儿童的整个社会关系中的行为。因此,这项工作的目的是定义最佳策略是找到“目标”(即,可以启动行为变革的有影响力的儿童)的身体活动(PA)干预,这将影响荷兰学校人口的平均PA课程。根据MyMovez项目的数据集,我们在社交网络中的影响范围基于儿童同行的影响,根据MyMovez项目 - 第I期的数据进行了调整。实施了五个干预策略,并比较了它们的疗效。选择目标后,将增加17%的初始PA。然后,运行扩散模型以验证一年后整个网络的PA的改进。我们讨论了模拟结果的影响,这些结果可以用于对社交网络干预和未来模型改进的设置进行明智的选择。我们的结果表明,针对更脆弱的儿童(即在更糟糕的环境中)并应用网络优化算法是该数据集的最佳解决方案,指示未来的干预应该瞄准这两种策略。

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