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Objective: In this study, unique body mass index (BMI) trajectories from ages 1 to 20 years were identified; each trajectory according to socio demographic and family characteristics was described., Methods: Participants came from two national population surveys (n = 7,253; n = 901) and were aged 1-6 years at baseline. Children were surveyed biennially over eight waves up to 14-20 years of age. BMI trajectories by sex and survey cohort were identified by group-based trajectory modeling. After crossvalidating trajectories between survey cohorts, the characteristics of trajectory membership were assessed by multinomial regression., Results: Four BMI trajectories were found: low, decreasing, medium, and high. The decreasing trajectory was characterized by an overweight or obese childhood followed by a normal-weight adolescence. The low, medium, and high trajectories were characterized by growth curves culminating, by age 20, to BMI 22.6, 29.3, and 34.9 kg/m(2) , respectively, for males and 20.6, 24.5, and 32.0 kg/m(2) , respectively, for females. Factors associated with the high trajectory included ethnicity and paternal education (female only), large for gestational age, rural area residence, and maternal smoking., Conclusions: The identification and validation of four major trajectories reflect the heterogeneity in patterns of BMI development from 1 to 20 years., (© 2015 The Obesity Society.)

Background: Few studies have evaluated the effect of adherence to a lifestyle intervention on adolescent health outcomes. The objective of this study was to determine whether adolescent and parental adherence to components of an e-health intervention resulted in change in adolescent body mass index (BMI) and waist circumference (WC) z-scores in a sample of overweight/obese adolescents.Methods: In total, 159 overweight/obese adolescents and their parents participated in an 8-month e-health lifestyle intervention. Each week, adolescents and their parents were asked to login to their respective website and to monitor their dietary, physical activity, and sedentary behaviours. We examined participation (percentage of webpages viewed [adolescents]; number of weeks logged in [parents]) and self-monitoring (number of weeks behaviors were tracked) rates. Linear mixed models and multiple regressions were used to examine change in adolescent BMI and WC z-scores and predictors of adolescent participation and self-monitoring, respectively.Results: Adolescents and parents completed 28% and 23%, respectively, of the online component of the intervention. Higher adolescent participation rate was associated with a decrease in the slope of BMI z-score but not with change in WC z-score. No association was found between self-monitoring rate and change in adolescent BMI or WC z-scores. Parent participation was not found to moderate the relationship between adolescent participation and weight outcomes.Conclusions: Developing strategies for engaging and promoting supportive interactions between adolescents and parents are needed in the e-health context. Findings demonstrate that improving adolescents' adherence to e-health lifestyle intervention can effectively alter the weight trajectory of overweight/obese adolescents. [ABSTRACT FROM AUTHOR]