Temporal Physical Activity Patterns and Association with Health Status Indicators and Chronic Disease. (29th May 2020)

• Record Type: Journal Article
• Authors: Eicher-Miller, Heather; Aqeel, Marah; Guo, Jiaqi; Gelfand, Saul; Delp, Edward; Bhadra, Anindya; Richards, Elizabeth; Hennessy, Erin; Lin, Luotao
• Is Part Of: Current developments in nutrition. Volume 4(2020)Supplement 2
• Page Start: 1166
• Page End: 1166
• Publication Date: 2020-05-29
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzaa056_013
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: Integration of time to physical activity (PA) patterns is novel and may be linked to health. Temporal PA patterns integrating time and PA were created to determine their association with health indicators: body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure, and chronic diseases obesity, diabetes, and metabolic syndrome in US adults 20–65 years. Methods: Objectively measured PA accelerometry data collected from the National Health and Nutrition Examination Survey 2003–2006 was used to pattern absolute PA intensity and time of activity from 1627 non-pregnant adults with one random valid PA day. Modified dynamic time warping and kernel k-means clustering grouped individuals representing temporal PA patterns. Multivariate regression models controlling for potential confounders and adjusting for multiple comparisons ( P < 0.05/6) determined associations between clusters and health outcomes. Results: A cluster representing a temporal PA pattern with the highest average intensity peaking at 11:00 and tapering off through the day was associated with lower BMI ( P < 0.0001), WC ( P < 0.0001), and 65% lower odds of obesity compared to a cluster with lower average PA intensity peaking at 12:00 and tapering off through the day (95%CI: 0.2, 0.5) and a cluster with the lowest average PA intensity with no distinct activity peaks (95%CI: 0.2, 0.4). Another cluster with a temporal PA pattern with high average PA intensity peaking at 19:00 was associated with lower BMI ( P = 0.0003), WC ( P = 0.001), and 60% lower odds of obesity compared to a cluster with lower average PA intensity peaking at 12:00 and tapering off through the day (95%CI: 0.2, 0.7) and a cluster with the lowest average PA intensity with no distinct peaks (95%CI: 0.2, 0.6). Conclusions: Temporal PA patterns are associated with differences in US adult health outcomes. Two clusters with the highest level of PA (peak at 11:00 or 19:00) were associated with significantly lower BMI and WC and lower odds of obesity compared to two other clusters with the lowest average PA intensity. Funding Sources: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health and Purdue University.