Behavioral Determinants of Childhood Obesity in the United States: An Exploratory Study

美国儿童肥胖的行为决定因素:一项探索性研究

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Abstract

Childhood obesity is a complex and multifactorial phenomenon. Understanding these factors is crucial in developing effective interventions to prevent and treat childhood obesity. The purpose of this study is to provide an update on factors related to childhood obesity in the United States. This cross-sectional study analyzed data from the 2021 Youth Risk Behavior Surveillance System (YRBSS) survey to assess factors associated with childhood obesity among US children aged 12 to 17 years. Logistic regression analysis was used to identify the sociodemographic factors associated with overweight and obesity. Data were analyzed using R studio (4.3.2). A total of 12,836 respondents were enrolled in this study. Among them, the prevalence of overweight, obesity, and morbid obesity was found to be 17.66%, 11.21%, and 1.76%, respectively. Respondents with a BMI over 25 were mostly male (17.63%) and of White race (32.77%). The main sociodemographic factors associated with overweight and obesity were being 14 or 15 years old, male, non-White, having a history of alcohol or marijuana consumption, and not practicing physical activity. These findings can inform targeted interventions for prevention and management. This research sheds light on critical sociodemographic factors related to childhood obesity in the U.S., highlighting its complexity. The findings emphasize the influence of age, gender, ethnicity, and lifestyle behaviors, such as substance use and physical inactivity, on obesity rates among youth. These insights are crucial for developing targeted interventions. Addressing these factors offers a real chance to enhance future health outcomes, and underscoring the need for comprehensive strategies that include both health education and broader community support to instill healthy habits early on. In addition, unexpected results concerning vegetable consumption and the omission of genetic and familial data suggest areas for further research.

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