Department of Psychology and Centre for the Advanced Study of Collective Behavior, Psychological Assessment and Health Psychology, University of Konstanz, Konstanz, Germany.
Public Health Nutrition, Paderborn University, Paderborn, Germany.
Adv Nutr. 2023 Sep;14(5):983-994. doi: 10.1016/j.advnut.2023.06.009. Epub 2023 Jul 5.
Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.
几乎所有个性化营养 (PN) 的方法都使用个体的基因变异等信息来提供比通用的“一刀切”建议更有益的建议。尽管人们对此充满热情，并且商业服务的可用性也有所增加，但到目前为止，科学研究仅揭示了个性化饮食建议的功效和效果的微小到可以忽略不计的影响，即使使用遗传或其他个体信息也是如此。此外，从公共卫生的角度来看，学者们对 PN 持批评态度，因为它主要针对社会特权群体，而不是普通人群，从而可能扩大健康不平等。因此，在这种观点下，我们建议通过创建适应个性化营养建议系统 (APNAS) 来扩展当前的 PN 方法，该系统根据个人在现实食物环境中的需求、能力和接受程度，针对个性化建议的类型和时间进行定制。这些系统扩展了当前 PN 的目标（即应该实现什么），将“个人目标偏好”纳入当前倡导的生物医学目标之外（例如，做出可持续的食物选择）。此外，它们涵盖了“行为改变的个性化过程”，通过在现实生活环境中提供现场的、“即时”信息（如何以及何时改变），考虑到个人的能力和限制（例如，经济资源）。最后，它们关注的是个人和专家之间的“参与式对话”（例如，实际或虚拟的营养师、营养学家和顾问），以设定目标和得出适应措施。在这个框架内，新兴的数字营养生态系统能够在从暴露到消费的食物环境中提供持续的、实时的监测、建议和支持。我们提出了这种新型 PN 框架的愿景，以及描述其有效满足个人和群体需求以及最受益于其实施的目标群体的潜力的场景和论点。
