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Towards a Data-Driven Approach to Intervention Design: A Predictive Path Model of Healthy Eating Determinants

机译:迈向干预设计的数据驱动方法:健康饮食决定因素的预测路径模型

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Dietary behavior and attitude play major roles in the worldwide prevalence of obesity, as weight is gained when energy intake exceeds energy expenditure. Although research has focused on designing technological interventions for healthy eating behavior, recent reviews have identified a gap in the knowledge base regarding the variables/determinants of healthy eating and the interactions between them. We developed a model of some determinants and their impact on healthy eating as a basis for designing technological interventions to promote healthy eating behavior within a target community. The main goal of this work is to understand how people adopt a healthy eating attitude, the variables influencing such attitudes, the interactions between these variables, and the degree of influence each variable exerts on healthy eating attitudes. We use fast food-related eating behavior as our case study. Our model shows that weight concern, nutrition knowledge, concern for diseases, social influence, and food choice motives predicts 65% of the variance in healthy eating attitudes, showing the suitability of the model for use in predicting healthy eating attitude. This result will inform decisions on the most effective persuasive strategy for designing interventions to promote healthy eating behavior.
机译:饮食行为和态度在全球肥胖症的普遍存在中发挥着重要作用,因为当能量摄入超过能源支出时,就获得了体重。虽然研究侧重于设计健康饮食行为的技术干预,但近期的评价已经确定了关于健康饮食的变量/决定因素的知识库的差距和它们之间的相互作用。我们开发了一些决定因素的模型及其对健康饮食的影响,作为设计技术干预措施,以促进目标社区内的健康饮食行为的基础。这项工作的主要目标是了解人们如何采用健康的饮食态度,影响这种态度的变量,这些变量之间的相互作用,以及每个变量的影响程度对健康的饮食态度施加。我们使用与案例研究一起使用的快餐饮食行为。我们的模式表明,重量关注,营养知识,疾病的关注,社会影响和食品选择动机预测了健康饮食态度方差的65%,显示了模型用于预测健康饮食态度的适用性。这一结果将使决策提供关于设计干预措施最有效的说服战略,以促进健康饮食行为。

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