Background:

Sedentary behaviour, independent of physical activity, is a risk factor for impaired metabolic health and early mortality (1, 2). A limited number of cross-sectional studies have been conducted to determine if sedentary behaviour is associated with cardiorespiratory fitness (CRF) (3, 4). A study assessing change in sedentary behaviour with regard to CRF levels would advance the understanding of the association between sedentary behaviour and CRF.

Purpose:

The purpose of the study is to determine if high levels of sedentary behaviour are independently associated with CRF levels during childhood.

- Objective 1: To determine if changes in sedentary behaviour from 12- to 16-years influence CRF levels at 16-years, independent of physical activity levels.

- Objective 2: To determine if gender, socioeconomic status and adiposity categories modify the association between changes in accelerometry determined sedentary behavior and CRF levels, as children age over time.

Dependent Variable:

- CRF at 16-years

Independent Variable:

- Sedentary behaviour class trajectory

Covariates:

- Moderate-to-vigorous physical activity at 16-years (MVPA; mins/d(cubed)3600cpm)

- CRF at 9-years

- Fat mass at 16-years (DXA)

- Breast-feeding status

- Social class

- Maternal obesity

- Maternal smoking status

- Birth weight

- Length of gestation

Statistical Analysis:

Trajectory classes of sedentary behaviour will be identified using latent class growth analysis, using PROC TRAJ (SAS version 9.2). Consideration will be given to quadratic models to determine if the trajectory classes are linear or curvilinear over time. The final number of classes identified will be determined by comparing Bayesian Information Criterion (BIC) values. The resulting trajectory classes identified include individuals with the highest posterior probability of belonging to a particular trajectory class (5).

To address objective 1, analysis of covariance (ANCOVA) models will be conducted to determine if CRF levels at 16-years vary among the trajectory class of sedentary behaviour. The first ANCOVA model will adjust for CRF at 9-years-old, and the second ANCOVA model will additionally adjust for daily minutes of moderate-to-vigorous physical activity (MVPA; accelerometer counts >=3600cpm) at 16-years-old. The third model will additionally adjust for fat mass at 16-years. The forth model will additionally adjust for breast-feeding status, maternal obesity, social class and maternal smoking status. The sedentary behaviour trajectory class variable will be treated as a categorical variable throughout and Tukey adjustments will be made to account for multiple comparisons between the trajectory classes.

To address objective 2, interaction terms will be included in the ANCOVA models. Specifically, a gender x sedentary behaviour interaction term will be included to assess if the association between change in sedentary behaviour and CRF is different for boys and girls. Second, a social class x sedentary behaviour interaction term will be included to determine if the association between change in sedentary behaviour and CRF is different between social class categories. Finally, a fat mass x sedentary behaviour interaction term will be included to determine if the association between change in sedentary behaviour and CRF is different depending on the level of fat mass. All analyses will be using SAS (version 9.2) and the statistical significance level of alpha=0.05 will be used throughout.

Proposed Tables/Figures:

- Figure 1: Trajectory classes of sedentary behaviour from 12- to 16-years

- Table 1: Descriptive statistics by sedentary behaviour trajectory classes

- Table 2: Association between sedentary behaviour class and CRF at 16-years

- Table 3: Association between sedentary behaviour and CRF at 16-years by gender, socioeconomic status and adiposity categories

Target Journals:

- Medicine and Science in Sports and Exercise (MSSE)

- British Journal of Sports Medicine

References:

1. Katzmarzyk PT, Church TS, Craig CL, Bouchard C. Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc. May 2009;41(5):998-1005.
2. Ekelund U, Brage S, Froberg K, et al. TV viewing and physical activity are independently associated with metabolic risk in children: the European Youth Heart Study. PLoS Med. Dec 2006;3(12):e488.
3. Grund A, Krause H, Siewers M, Rieckert H, Muller MJ. Is TV viewing an index of physical activity and fitness in overweight and normal weight children? Public Health Nutr. Dec 2001;4(6):1245-1251.
4. Lobelo F, Dowda M, Pfeiffer KA, Pate RR. Electronic media exposure and its association with activity-related outcomes in female adolescents: cross-sectional and longitudinal analyses. J Phys Act Health. Mar 2009;6(2):137-143.
5. Andruff HC, N.; Thompson, A.; Gaudreau, P.; Louvet, B. Latent class growth modelling: a tutorial. Tutorials in Quantitative Methods for Psychology. 2009;5(1):11-24.