Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
UCD Institute of Food and Health, University College Dublin, Dublin, Ireland.
Adv Nutr. 2021 Jul 30;12(4):1365-1378. doi: 10.1093/advances/nmaa175.
There is a scarcity of dietary intake research focusing on the intake of whole meals rather than on the nutrients and foods of which those meals are composed. This growing area of research has recently begun to utilize advanced statistical techniques to manage the large number of variables and permutations associated with these complex meal patterns. The aim of this narrative review was to evaluate those techniques and the meal patterns they detect. The 10 observational studies identified used techniques such as principal components analysis, clustering, latent class analysis, and decision trees. They examined meal patterns under 3 categories: temporal patterns (relating to the timing and distribution of meals), content patterns (relating to combinations of foods within a meal and combinations of those meals over a day), and context patterns (relating to external elements of the meal, such as location, activities while eating, and the presence or absence of others). The most common temporal meal patterns were the 3 meals/d pattern, the skipped breakfast pattern, and a grazing pattern consisting of smaller but more frequent meals. The 3 meals/d pattern was associated with increased diet quality compared with the other 2 patterns. Studies identified between 7 and 12 content patterns with limited similarities between studies and no clear associations between the patterns and diet quality or health. One study simultaneously examined temporal and context meal patterns, finding limited associations with diet quality. No study simultaneously examined other combinations of meal patterns. Future research that further develops the statistical techniques required for meal pattern analysis is necessary to clarify the relations between meal patterns and diet quality and health.
目前针对整体膳食摄入的研究相对较少，而大多聚焦于营养素和食物成分的摄入。最近，这一不断发展的研究领域开始采用先进的统计技术来处理与这些复杂膳食模式相关的大量变量和组合。本综述旨在评估这些技术以及它们所检测到的膳食模式。10 项观察性研究采用了主成分分析、聚类分析、潜在类别分析和决策树等技术。它们从 3 个方面研究了膳食模式：时间模式（与膳食的时间和分布有关）、内容模式（与膳食内食物的组合以及一天中这些膳食的组合有关）和环境模式（与膳食的外部因素有关，如就餐地点、就餐时的活动以及是否有其他人在场）。最常见的时间性膳食模式是一日三餐模式、不吃早餐模式和少食多餐模式。与另外两种模式相比，一日三餐模式与更高的饮食质量有关。研究共确定了 7 至 12 种内容模式，但各研究之间的相似性有限，且这些模式与饮食质量或健康之间没有明确的关联。有一项研究同时考察了时间性和环境性膳食模式，发现与饮食质量的相关性有限。没有研究同时考察其他组合的膳食模式。为了阐明膳食模式与饮食质量和健康之间的关系，需要进一步开发用于膳食模式分析的统计技术。
