Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.
Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.
复制标题
模拟动态食物选择过程以了解饮食干预效果。
DOI：
10.1093/abm/kax041
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Persky,Susan
中科院分区：
文献类型：
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作者：
Marcum,ChristopherSteven;Goldring,MeganR;McBride,ColleenM;Persky,Susan
背景膳食构建在很大程度上受无意识和习惯性过程的支配，这些过程可以表示为最终产生最终盘子的个体微观水平食物选择的集合。尽管如此，饮食行为干预研究很少捕捉这些微观层面的食物选择过程，而是在总体水平上衡量结果。这部分是由于缺乏分析技术来模拟这些动态的时间序列events.PurposeThe当前的文章解决了这一限制，通过应用关系事件框架的泛化来模拟微观层面的食物选择行为以下的教育intervention.MethodRelational事件建模被用来模拟食物的选择，221母亲为他们的孩子后收到的信息为基础的干预。参与者被随机分配接受（a）对照信息;（B）儿童肥胖风险信息;（c）儿童肥胖风险信息加上个性化的基于家族史的风险评估。然后，与会者为他们的孩子在虚拟现实为基础的食品自助餐simulation.ResultsMicro-level方面的建成环境，如自助餐中的每种食物的顺序，食品的选择是有影响力的。其他动态过程，如选择惯性也影响食物选择。在接受最强的干预条件的参与者中，选择惯性下降，食物选择的整体速度increased.ConclusionsModeling食物选择过程可以阐明干预措施发挥其影响力的点。研究人员可以利用这些发现来深入了解影响饮食结果的食物选择的无意识和不可控方面，这最终可以改善饮食干预措施的设计。
BackgroundMeal construction is largely governed by nonconscious and habit-based processes that can be represented as a collection of in dividual, micro-level food choices that eventually give rise to a final plate. Despite this, dietary behavior intervention research rarely captures these micro-level food choice processes, instead measuring outcomes at aggregated levels. This is due in part to a dearth of analytic techniques to model these dynamic time-series events.PurposeThe current article addresses this limitation by applying a generalization of the relational event framework to model micro-level food choice behavior following an educational intervention.MethodRelational event modeling was used to model the food choices that 221 mothers made for their child following receipt of an information-based intervention. Participants were randomized to receive either (a) control information; (b) childhood obesity risk information; (c) childhood obesity risk information plus a personalized family history-based risk estimate for their child. Participants then made food choices for their child in a virtual reality-based food buffet simulation.ResultsMicro-level aspects of the built environment, such as the ordering of each food in the buffet, were influential. Other dynamic processes such as choice inertia also influenced food selection. Among participants receiving the strongest intervention condition, choice inertia decreased and the overall rate of food selection increased.ConclusionsModeling food selection processes can elucidate the points at which interventions exert their influence. Researchers can leverage these findings to gain insight into nonconscious and uncontrollable aspects of food selection that influence dietary outcomes, which can ultimately improve the design of dietary interventions.