ABSTRACT
OBJECTIVE: The purpose of this study is to identify distinct body mass index (BMI) trajectories associated with weight classification, and to examine demographic characteristics and clusters of obesogenic behaviours in adolescents with these trajectories. METHODS: Data were extracted from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n = 1,006, Grades 5-8). The independent variables were physical activity (accelerometer and child report), sports participation, television/video watching time and recreational computer use. The dependent variable was raw BMI. Growth mixture modelling, mixture modelling and independent t-test analyses were used. RESULTS: Two distinct BMI trajectories were identified - one with the mean BMI within the Overweight-Obese classification (≥85th percentile) and the other within the healthy weight classification (5th- 84th percentile). Two clusters of physical and sedentary behaviours were identified in adolescents with the Overweight-Obese BMI trajectory. These clusters differed in the type of sedentary behaviour (computer vs. television/video). Three clusters were identified in adolescents with the Healthy Weight BMI trajectory. These clusters differed in levels of physical activity and types of sedentary behaviour. CONCLUSION: This study contributes to the understanding of multi-dimensional obesogenic behavioural patterns and highlights the importance of understanding types of sedentary behaviour in adolescents.
ABSTRACT
BACKGROUND: The relationships between environmental correlates of adolescent obesity are complex and not yet well defined by current research, especially when considering age and gender. OBJECTIVE: The purpose of this study was to test a model of proximal (home) and distal (neighbourhood) environmental correlates of obesity for adolescent age and gender groups. METHODS: This was a descriptive, cross-sectional study, using the 2007 National Survey of Children's Health of 39 542 children ages 11-17 years. RESULTS: The model fit the data well for early adolescents (ages 11-14 years) (root mean square standard error of approximation [RMSEA] 0.040, 90% confidence interval [CI]: 0.039-0.041; comparative fit index [CFI] 0.947; Tucker-Lewis index [TLI] 0.929) and middle adolescents (ages 15-17 years) (RMSEA 0.037, 90% CI: 0.036-0.038; CFI 0.052; TLI 0.937). The model also fit the data well for boy adolescents (RMSEA 0.038, 90% CI: 0.037-0.039; CFI 0.951; TLI 0.935) and girl adolescents (RMSEA 0.038, 90% CI: 0.037-0.040; CFI 0.949; TLI 0.932). CONCLUSIONS: All models provide loadings of the environmental correlates of adolescent obesity for specific age and gender groups that can be used for early identification of risks and targeted interventions.
