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1.
Soc Sci Med ; 189: 145-151, 2017 09.
Article in English | MEDLINE | ID: mdl-28768573

ABSTRACT

OBJECTIVE: To test whether exposure to violence is associated with same-day increases in obesogenic behaviors among young adolescents, including unhealthy food and beverage consumption, poor quality sleep, and lack of physical activity. METHODS: Young at-risk adolescents between 12 and 15 years of age were recruited via telephone screening from low-income neighborhoods. Adolescents and their parents completed in-person assessments, followed by Ecological Momentary Assessment (EMA) delivered to 151 adolescents' mobile phones three times a day for 30 days (4329 person days). Three obesogenic behaviors - unhealthy food consumption, poor sleep quality, and lack of physical activity - and violence exposure were assessed daily. Adolescents' body mass index (BMI) was assessed prior to the EMA and 18 months later. A replication was performed among 395 adolescents from a population-representative sample (with 5276 EMA person days). RESULTS: On days that at-risk adolescents were exposed versus not exposed to violence, they were more likely to consume unhealthy foods and beverages (b = 0.12, p = 0.01), report feeling tired the next morning (OR = 1.58, p < 0.01), and to be active (OR = 1.61, p < 0.01). At-risk adolescents who reported higher consumption of soda and caffeinated beverages during the 30-day EMA were more likely to experience increases in BMI in later adolescence. Findings related to sleep and activity were supported in the population-based replication sample; however, no significant same-day associations were found between violence exposure and unhealthy dietary consumption. CONCLUSIONS: This study provides evidence that exposure to violence is associated with same-day unhealthy dietary consumption among at-risk adolescents and next-day tiredness related to sleep quality among adolescents from both at-risk and normative populations. Findings also point to unhealthy soda consumption during early adolescence as an important predictor of weight gain among at-risk adolescents.


Subject(s)
Adolescent Behavior/psychology , Exposure to Violence/psychology , Feeding Behavior/psychology , Adolescent , Body Mass Index , Child , Exercise/psychology , Female , Health Behavior , Humans , Male , Obesity/prevention & control , Obesity/psychology , Sedentary Behavior , Social Class
2.
J Sch Health ; 86(12): 864-872, 2016 12.
Article in English | MEDLINE | ID: mdl-27866387

ABSTRACT

BACKGROUND: Schools are important sites for interventions to prevent childhood obesity. This study examines how variables measuring the socioeconomic and racial composition of schools and counties affect the likelihood of obesity among third to fifth grade children. METHODS: Body mass index data were collected from third to fifth grade public school students by teachers from 317 urban and rural North Carolina schools in 38 counties. Multilevel models are used to examine county-, school-, and individual-level effects. RESULTS: Low concentrations of poverty at the school level are associated with lower odds of obesity. Schools in rural counties had significantly higher rates of obesity, net the other variables in the model. Students in minority-segregated schools had higher rates of obesity than those in more racially diverse schools, but the effect was not statistically significant once school-level poverty was controlled. CONCLUSIONS: Place-based inequalities are important determinants of health inequalities. The results of this study show that school-level variables related to poverty are important for understanding and confronting childhood obesity.


Subject(s)
Pediatric Obesity/epidemiology , Poverty/statistics & numerical data , Racial Groups/statistics & numerical data , Schools/statistics & numerical data , Social Segregation , Body Mass Index , Child , Female , Food Assistance/statistics & numerical data , Food Supply , Humans , Male , North Carolina/epidemiology , Pediatric Obesity/ethnology , Residence Characteristics/statistics & numerical data , Risk Factors , Rural Population/statistics & numerical data , Socioeconomic Factors , Urban Population/statistics & numerical data
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