ArticleThe American journal of clinical nutrition2026
Identifying temporal eating patterns: a comparison of latent class analysis and dynamic time warping-based cluster analysis.
Article in The American journal of clinical nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTemporal eating patterns (TEPs) are associated with diet quality and obesity, although inconsistencies exist because of variations in methods and input variables. Direct comparisons of analytical approaches for deriving TEPs are rare.
objectivesThe aim of this study was to compare latent class analysis (LCA) and modified dynamic time warping (MDTW)-based cluster analysis for deriving TEPs and examine their associations with diet quality and obesity.
methodsThis cross-sectional study included 672 adults (18-65 y) in Victoria, Australia, from the "EveryDayLife" survey (2017-2020). Participants completed a 1- to 7-d food diary via the "FoodNow" app. LCA used hourly presence/absence of eating occasions (EOs), whereas MDTW-based clustering used hourly energy intake (EI) input variables. Methods were compared using patterns visualization, membership overlap, kappa statistics, adjusted R
resultsBoth methods identified 3 distinct TEPs. Class 1/Cluster 1 had peaks during 07:00-09:00, 12:00, and 18:00-19:00 h ("conventional" pattern). Class 2/Cluster 2 had later peaks in EOs or EI (after 13:00 h). Class 3/Cluster 3 showed modest, evenly spaced EOs or EI concentrated earlier in the day. Membership overlap between similar TEPs was 56.2%-73.1%, with fair agreement (κ = 0.38, P < 0.001). Class 1/Cluster 1 showed higher diet quality than Class 2/Cluster 2, respectively, whereas no significant associations were observed with BMI. LCA explained slightly more variance in diet quality (6% compared with 4%) compared with MDTW-based clustering, with a similar proportion observed for BMI (∼13%). The AUCs for discriminating high diet quality (LCA: 0.635 compared with MDTW: 0.616; P = 0.565) and obesity (LCA: 0.758 compared with MDTW: 0.756; P = 0.934) were not significantly different between the 2 methods.
conclusionsLCA and MDTW-based clustering identified comparable yet noninterchangeable TEPs, suggesting both approaches are suitable for deriving TEPs.
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