Distance metrics optimized for clustering temporal dietary patterning among U.S. adults. (1st January 2020) - Eicher-Miller, Heather A.; Gelfand, Saul; Hwang, Youngha; Delp, Edward; Bhadra, Anindya; Guo, Jiaqi
Abstract: Objective: Few attempts to determine dietary patterns have incorporated concepts of time, specifically time and proportion of energy intake consumed throughout a day. A type of modified dynamic time warping (MDTW) was previously developed using an appropriate distance metric for patterning these aspects to determine temporal dietary patterns (TDP). This study further explores dynamic time warping (DTW) distance metrics including unconstrained DTW (UDTW), constrained DTW (CDTW), and MDTW with modern spectral clustering methods to optimize TDP related to dietary quality. MDTW was expected to create TDP with the strongest relationships to dietary quality and distinct visualization among U.S. adults 20-65y of the National Health and Nutrition Examination Survey 1999–2004. Methods: Proportional energy intake by time of day metrics were optimized to create TDP from complete day-one 24-h dietary recalls using MDTW, UDTW with only a standard local constraint, and CDTW with standard local and global banding constraints, then clustered using spectral clustering. The association between each TDP distance metric clustering and mean dietary quality, as indicated by the 2005 Healthy Eating Index (HEI-2005), were determined using multiple linear regression controlled for potential confounders. Strength of association for each model was compared using adjusted R-squared. The results were also visualized to make qualitative comparisons. Results: Four clusters representing distinct TDP for each distance metric by spectral clustering were generated among participants. MDTW exhibited TDP clusters with strongest associations to HEI compared with the TDP clusters generated from unconstrained and constrained DTW, and visualization of the TDP clusters from MDTW supported the association. Implication: MDTW paired with spectral clustering is a useful tool for dimension reduction and uncovering temporal patterns with dietary data.