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.
View Article and Find Full Text PDFIn 2019, Health Canada released a new iteration of Canada's Food Guide (2019-CFG), which, for the first time, highlighted recommendations regarding eating practices, i.e., guidance on where, when, why, and how to eat.
View Article and Find Full Text PDFThe Canadian Food Intake Screener was developed to rapidly assess alignment of dietary intake with the Canada's Food Guide-2019 healthy food choices recommendations. Scoring is aligned with the Healthy Eating Food Index-2019 to the extent possible. Among a sample of adults, reasonable variation in screener scores was noted, mean screener scores differed between some subgroups with known differences in diet quality, and a moderate correlation between screener scores and total Healthy Eating Food Index-2019 scores based on repeat 24 h dietary recalls was observed.
View Article and Find Full Text PDFInterventions are urgently needed to transform the food system and shift population eating patterns toward those consistent with human health and environmental sustainability. Postsecondary campuses offer a naturalistic setting to trial interventions to improve the health of students and provide insight into interventions that could be scaled up in other settings. However, the current state of the evidence on interventions to support healthy and environmentally sustainable eating within postsecondary settings is not well understood.
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