Joint Temporal Dietary and Physical Activity Patterns: Associations with Health Status Indicators and Chronic Diseases. (29th May 2020)

• Record Type: Journal Article
• Authors: Lin, Luotao; Aqeel, Marah; Guo, Jiaqi; Gelfand, Saul; Delp, Edward; Bhadra, Anindya; Richards, Elizabeth; Hennessy, Erin; Eicher-Miller, Heather
• Is Part Of: Current developments in nutrition. Volume 4(2020)Supplement 2
• Page Start: 590
• Page End: 590
• Publication Date: 2020-05-29
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzaa047_010
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: The aims of this study are to (1) create clusters of joint temporal patterns of diet and physical activity (PA), and (2) determine the association of these clusters with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure and health outcomes including obesity, Type 2 Diabetes (T2DM) and metabolic syndrome (MS) in U.S. adults 20–65 years. Methods: A random day of PA from accelerometry data and the first day 24 hour dietary recall collected in the National Health and Nutrition Examination Survey 2003–2006 were used to determine absolute PA intensity, absolute energy intake, and the time of these activities. Dietary and PA data from 1, 627 U.S. adults were Z-normalized. Dynamic time warping (DTW) coupled with kernel-k means clustering algorithm was used to develop joint temporal dietary and PA patterns that maximally partition individuals with similar temporal behaviors into mutually exclusive clusters derived from the data rather than predefined standards. Multivariate regression models adjusted for ptential confounders, multiple comparisons and survey design factors determined associations between joint temporal patterns and health status indicators along with health outcomes ( P < 0.05/6). Results: Significant differences of health status indicators and health outcomes were discovered among four clusters. A cluster, representing a joint temporal dietary and PA pattern with proportionally equivalent average energy consumed with two energy intake peaks at 1 PM and 8 PM and the lowest PA intensity compared to all other clusters, was associated with significantly higher BMI (β: 3.5, P < 0.0001), WC (β: 9.5, P < 0.0001), and significantly higher odds of obesity (Odds Ratio = 4.685, P < 0.0001) compared to a cluster with similar energy and intake peaks and the highest PA intensity compared to all other clusters. Conclusions: The joint temporal dietary and PA patterns discovered support previous evidence of the link of energy intake and PA on health outcomes. DTW coupled with kernel-k means clustering algorithm can be used to capture differences in temporal dietary and PA behaviors and hold promise for the future development of lifestyle patterns. Funding Sources: National Cancer Institute & Purdue University