Temporal physical activity patterns are associated with obesity in U.S. adults

Abstract

Background: Few attempts have been made to incorporate multiple aspects of physical activity (PA), including timing and volume, to classify patterns that link to health. Temporal PA patterns integrating time and activity counts were created to determine their association with health. Methods: PA accelerometry data obtained from the cross-sectional National Health and Nutrition Examination Survey 2003-2006 was used to pattern PA counts and time of activity from 1,999 nonpregnant adults with one random valid weekday of activity. Constrained dynamic time warping with Sakoe-Chiba band and kernel k-means clustering grouped participants to 4 clusters 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 status indicators and conditions obesity, type 2 diabetes, and metabolic syndrome.Results: Participants in Cluster 2, represented by a temporal PA pattern with activity counts reaching >1.2e 5 counts/ h (cph) and tapering off through the day, had lower mean body mass index (BMI) (p<0.001), waist circumference (WC) (p<0.01), and 65% lower odds of obesity relative to normal weight status compared with participants in Cluster 1 with the lowest PA counts reaching 4.8e 4 cph from 6:00 to 23:00 (OR: 0.3; 95% CI: 0.2, 0.8). Cluster 3, characterized by a temporal PA pattern with activity counts reaching 9.6e 4 -1.2e 5 cph between 16:00 to 21:00, was associated with lower mean BMI (p<0.001) and WC (p<0.01), and 60% lower odds of obesity relative to normal weight status compared to Cluster 1 (OR: 0.4; 95% CI: 0.2, 0.8). Cluster 4 characterized by activity counts reaching 9.6e 4 cph between 8:00 to 11:00 was associated with lower BMI and WC compared to Cluster 1 (both p<0.05).Conclusions: U.S. adults with temporal PA patterns of higher activity counts ranging between 9.6e 4 ->1.2e 5 cph performed early (8:00 to 11:00), late (16:00 to 21:00), or throughout the day had signi cantly lower mean BMI and WC compared with adults with a temporal PA pattern of the lowest PA counts reaching 4.8e 4 cph from 6:00 to 23:00. Temporal PA patterns created by integrating time with PA counts throughout a day meaningfully link to health status.

Twenty-four-hour activity-count behavior patterns associated with depressive symptoms: Cross-sectional study by a big data-machine learning approach

“…The ML is applied to identify or categorize behaviors such as sleeping, lying, sitting, standing, walking, ascending or stairs, running, and other routine activities like biking, household chores, and yoga [37]. However, few studies have directly extracted or categorized temporal physical activity patterns by ML but could not differentiate between the temporal physical activity pattern and the intensity [50] or the total amount of physical activity [51]. Our study focused on categorizing each 24-hour activity-count into several clusters directly and identifying the behavioral tendencies of each individual based on the probability of dominant activity patterns.…”

Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach

“…The ML is applied to identify or categorize behaviors such as sleeping, lying, sitting, standing, walking, ascending or stairs, running, and other routine activities like biking, household chores, and yoga [ 36 ]. However, few studies have directly extracted or categorized temporal physical activity patterns by ML but could not differentiate between the temporal physical activity pattern and the intensity [ 49 ] or the total amount of physical activity [ 50 ]. Our study focused on categorizing each 24-h activity-count into several clusters directly and identifying the behavioral tendencies of each individual based on the relative frequency of dominant activity patterns.…”

“…However, our approach in this study could be applicable to other diseases or disorders in which physical activity could be closely related to the diagnosis. Temporal physical activity patterns might also indicate a meaningful link to cardiovascular disease (CVD) and obesity [ 49 , 50 ]. Clustering temporal physical activity patterns by machine learning could enable comprehensive description or analysis at a higher resolution.…”

Twenty-four hour activity-count behavior patterns associated with depressive symptoms: A big data-machine learning approach

“…The ML is applied to identify or categorize behaviors such as sleeping, lying, sitting, standing, walking, ascending or stairs, running, and other routine activities like biking, household chores, and yoga [37]- [49]. However, few studies have directly extracted or categorized temporal physical activity patterns by ML but could not differentiate between the temporal physical activity pattern and the intensity [55] or the total amount of physical activity [56]. Our study focused on categorizing each 24-hour activity-count into several clusters directly and identifying the behavioral tendencies of each individual based on the probability of dominant activity patterns.…”