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Background: The family is the main environment where children are socialized and learn individual behavior. Although previous studies have examined predictors of preadolescent first alcohol use, few studies have analyzed factors associated with alcohol use in children in a country with low alcohol consumption. The aim of this study was to investigate the initiation of alcohol use by sixth graders and determine family factors associated with first alcohol use.

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Peer victimization and sleep problems partly explain the link between BMI and depressive symptoms. Interventions to prevent or manage depressive symptoms may yield better results if they consider the effects of these two psychosocial factors rather than targeting BMI alone.

This study explored developmental trajectory patterns of BMI and associated factors. Participants included 1,609 students who were followed from age 7 to 12 years. Data collection involved annual self-administered questionnaires and records of height and weight. An ecological model was used to identify the factors associated with BMI trajectories. Group-based trajectory models and multinomial logit models were used in the statistical analysis. There were gender differences in BMI trajectories. Among boys, four BMI trajectories were normal or slightly underweight, persistently normal weight, overweight becoming obese, and persistently obese. Among girls, four BMI trajectories were persistently slightly underweight, persistently normal weight, persistently overweight, and persistently obese. The mean BMI in each trajectory group demonstrated an upward trend over time. In boys, BMI trajectories were significantly associated with after-school exercise, academic performance, family interactions, overweight parents, and father's education level. In girls, BMI trajectories were significantly associated with television viewing or computer use, family interactions, peer interactions, and overweight parents. Children under age 7 years who are already overweight or obese are an important target for interventions. The different factors associated with BMI trajectories can be used for targeting high risk groups.

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