个性化的饮食建议越来越受欢迎,然而,目前大多数方法都是基于个体的遗传和表型特征,而在很大程度上忽略了其他决定因素,如社会经济和认知变量。本文通过测试同时针对个人的社会人口群体量身定制的个性化健康饮食建议的有效性,提供了新颖的见解。认知特征,和感官偏好。我们首先使用现有数据基于来自3654个家庭的信息构建合成数据集(研究1a),然后开发了一个聚类模型来识别具有相似社会人口统计特征的个体,认知,和感官方面(研究1b)。最后,在研究2中,我们使用8个集群的特征来构建8个独立的个性化食物选择建议,并评估它们促进水果和蔬菜消费增加以及饱和脂肪和糖摄入量减少的能力。我们向218位参与者提供了通用的英国政府“EatWell”建议,为他们分配的集群量身定制的建议(匹配的个性化),或为不同的集群量身定制的建议(无与伦比的个性化)。结果表明,与一般建议相比,接受匹配的个性化建议的参与者更有可能表明他们会改变饮食.当参与者收到无与伦比的个性化建议时,他们同样有动机增加蔬菜消费量和减少饱和脂肪摄入量。可能凸显提供替代食物选择的力量。总的来说,这项研究表明,个性化食物选择建议的力量,基于个体特征的组合,在推动饮食改变方面比目前的方法更有效。我们的研究还强调了通过自动提供基于网络的个性化建议来解决人口健康问题的可行性。
Personalised dietary advice has become increasingly popular, currently however most approaches are based on an individual\'s genetic and phenotypic profile whilst largely ignoring other determinants such as socio economic and cognitive variables. This paper provides novel insights by testing the effectiveness of personalised healthy eating advice concurrently tailored to an individual\'s socio-demographic group, cognitive characteristics, and sensory preferences. We first used existing data to build a synthetic dataset based on information from 3654 households (Study 1a), and then developed a cluster model to identify individuals characterised by similar socio-demographic, cognitive, and sensory aspects (Study 1b). Finally, in Study 2 we used the characteristics of 8 clusters to build 8 separate personalised food choice advice and assess their ability to motivate the increased consumption of fruit and vegetables and decreased intakes of saturated fat and sugar. We presented 218 participants with either generic UK Government \"EatWell\" advice, advice that was tailored to their allocated cluster (matched personalised), or advice tailored to a different cluster (unmatched personalised). Results showed that, when compared to generic advice, participants that received matched personalised advice were significantly more likely to indicate they would change their diet. Participants were similarly motivated to increase vegetable consumption and decrease saturated fat intake when they received unmatched personalised advice, potentially highlighting the power of providing alternative food choices. Overall, this study demonstrated that the power of personalizing food choice advice, based on a combination of individual characteristics, can be more effective than current approaches in motivating dietary change. Our study also emphasizes the viability of addressing population health through automatically delivered web-based personalised advice.