Sedentary Behavior Patterns: A Latent Class Trajectory Analysis of Within-Day SB Patterns
Principal Investigator
Name
Margaret Damare
Degrees
B.A.
Institution
The University of North Carolina at Chapel Hill
Position Title
Research Assistant - Master's Student
Email
About this CDAS Project
Study
IDATA
(Learn more about this study)
Project ID
IDATA-60
Initial CDAS Request Approval
Dec 5, 2022
Title
Sedentary Behavior Patterns: A Latent Class Trajectory Analysis of Within-Day SB Patterns
Summary
Prolonged sedentary behavior (SB), defined as very-low intensity behaviors in seated or reclined posture, is associated with increased cardiovascular disease (CVD) risk and overall mortality. Recently, latent class trajectory models (LCTM) grouped individuals by daily patterns of accelerometer-derived SB (% of the day in SB bouts) and reported a 2.1 greater risk of mortality in the most vs. least sedentary individuals. The proposed study will expand upon this research by investigating within-day SB patterns (% of each hour in SB) and determine the associations with markers of CVD risk. Total and bouted SB will be derived from ActivPAL data. Moderate to vigorous intensity physical activity will be derived Actigraph data respectively. Light-intensity activity will be calculated as the remaining activity time not accounted for by other behaviors (i.e. SB, MVPA, non-wear time). Within-day latent class distinctions will be made based on the % of each hour spent in each behavior throughout the day, with data for each hour averaged across the week (e.g., 10:00 – 11:00 AM, Monday to Sunday). We will also explore classes in weekdays only (Mon-Fri) and weekend days only (Sat & Sun). Next, linear regression will be used to assess relationships between class membership and CVD risk markers (body mass index, BMI; waist circumference, WC). This study will introduce a novel methodology for investigating SB that logically expands on current practices, while providing valuable information related to the daily interaction between movement behaviors and CVD risk.
Aims
Primary Aim: Use latent class trajectory model (LCTM) analysis of accelerometry data (Actigraph, ActivPAL) to establish class distinctions based on patterns of % sedentary time by hour throughout the day.
Secondary Aim: Investigate associations between class membership and obesity indices (i.e. waist circumference and body mass index).
Collaborators
Lee Stoner PhD, The University of North Carolina at Chapel Hill Department of Exercise & Sport Science
Simon Higgins PhD, The University of North Carolina at Chapel Hill Department of Exercise & Sport Science