Advances in methods for characterizing dietary patterns: A scoping review.

ABSTRACT: There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class

SUBMITTER: Hutchinson JM 

PROVIDER: S-EPMC11213084 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature