Department of Nutrition Science, Purdue University, 700 West State Street, West Lafayette, IN, 47907, USA.
School of Electrical and Computer Engineering, 465 Northwestern Avenue, Purdue University, West Lafayette, IN, 47907, USA.
Appetite. 2020 Jan 1;144:104451. doi: 10.1016/j.appet.2019.104451. Epub 2019 Sep 12.
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.
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.
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.
MDTW paired with spectral clustering is a useful tool for dimension reduction and uncovering temporal patterns with dietary data.
很少有尝试确定饮食模式的方法结合了时间的概念，特别是全天摄入能量的时间和比例。之前使用适当的距离度量开发了一种改良的动态时间扭曲（MDTW），用于对这些方面进行模式化以确定时间膳食模式（TDP）。本研究进一步探索了动态时间扭曲（DTW）距离度量，包括无约束 DTW（UDTW）、约束 DTW（CDTW）和 MDTW 与现代光谱聚类方法相结合，以优化与饮食质量相关的 TDP。预计 MDTW 会创建与饮食质量相关性最强的 TDP，并在美国成年人中具有独特的可视化效果 20-65y 的 1999-2004 年国家健康与营养检查调查。
通过时间的比例能量摄入指标进行优化，使用 MDTW、仅具有标准局部约束的 UDTW 和具有标准局部和全局带约束的 CDTW 从完整的一天 24 小时饮食回忆中创建 TDP，然后使用光谱聚类进行聚类。使用多元线性回归控制潜在混杂因素，确定每个 TDP 距离度量聚类与平均饮食质量（由 2005 年健康饮食指数（HEI-2005）表示）之间的关联。使用调整后的 R 平方比较每个模型的关联强度。结果也进行了可视化，以便进行定性比较。
在参与者中，通过光谱聚类为每个距离度量生成了代表不同 TDP 的四个聚类。与无约束和约束 DTW 生成的 TDP 聚类相比，MDTW 表现出与 HEI 最强关联的 TDP 聚类，并且 MDTW 生成的 TDP 聚类的可视化支持了这种关联。
MDTW 与光谱聚类相结合是一种有用的工具，可用于减少维度并揭示饮食数据中的时间模式。
