Rowett Institute, University of Aberdeen, United Kingdom.
Institute of Applied Health Sciences, University of Aberdeen, United Kingdom.
Adv Nutr. 2021 Jun 1;12(3):579-589. doi: 10.1093/advances/nmaa173.
Personalized and precision nutrition aim to examine and improve health on an individual level, and this requires reconsideration of traditional dietary interventions or behavioral study designs. The limited frequency of measurements in group-level human nutrition trials cannot be used to infer individual responses to interventions, while in behavioral studies, retrospective data collection does not provide an accurate measure of how everyday behaviors affect individual health. This review introduces the concept of N-of-1 study designs, which involve the repeated measurement of a health outcome or behavior on an individual level. Observational designs can be used to monitor a participant's usual health or behavior in a naturalistic setting, with repeated measurements conducted in real time using an Ecological Momentary Assessment. Interventional designs can introduce a dietary or behavioral intervention with predictors and outcomes of interest measured repeatedly either during or after 1 or more intervention and control periods. Due to their flexibility, N-of-1 designs can be applied to both short-term physiological studies and longer-term studies of eating behaviors. As a growing number of disease markers can be measured outside of the clinic, with self-reported data delivered via electronic devices, it is now easier than ever to generate large amounts of data on an individual level. Statistical techniques can be utilized to analyze changes in an individual or to aggregate data from sets of N-of-1 trials, enabling hypotheses to be tested on a small number of heterogeneous individuals. Although their designs necessitate extra methodological and statistical considerations, N-of-1 studies could be used to investigate complex research questions and to study underrepresented groups. This may help to reveal novel associations between participant characteristics and health outcomes, with repeated measures providing power and precision to accurately determine an individual's health status.
个性化和精准营养旨在从个体层面上检查和改善健康，这需要重新考虑传统的饮食干预或行为研究设计。在群体水平的人类营养试验中，测量的频率有限，无法用来推断个体对干预的反应，而在行为研究中，回顾性数据收集并不能准确衡量日常行为如何影响个体健康。本综述介绍了 N-of-1 研究设计的概念，它涉及在个体水平上重复测量健康结果或行为。观察性设计可用于在自然环境中监测参与者的通常健康或行为，使用生态瞬时评估实时进行重复测量。干预性设计可以引入饮食或行为干预，在 1 个或多个干预和对照期内或之后重复测量感兴趣的预测因素和结果。由于其灵活性，N-of-1 设计可应用于短期生理研究和长期饮食行为研究。由于越来越多的疾病标志物可以在诊所外测量，并且通过电子设备提供自我报告数据，因此现在比以往任何时候都更容易在个体水平上生成大量数据。统计技术可用于分析个体的变化，或汇总来自多套 N-of-1 试验的数据，从而可以在少数异质个体上检验假设。尽管它们的设计需要额外的方法学和统计学考虑，但 N-of-1 研究可以用于研究复杂的研究问题和代表性不足的群体。这可能有助于揭示参与者特征与健康结果之间的新关联，重复测量提供了力量和精度，可准确确定个体的健康状况。
