2019
DOI: 10.3389/fpubh.2019.00301
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Characterizing Micro-scale Disparities in Childhood Obesity: Examining the Influence of Multilevel Factors on 4-Year Changes in BMI, Healthy Eating, and Physical Activity, Among a Cohort of Children Residing in Disadvantaged Urban Enclaves

Abstract: The childhood obesity epidemic in the United States disproportionately affects minority, low-income populations. Hispanics have one of the highest childhood obesity rates, and are the fastest growing population subgroup in the country. Past research has examined disparities in the occurrence of obesity, healthy eating, and physical activity at a macro-geographic level, with less emphasis on examining the multilevel, micro-scale determinants of childhood obesity in disadvantaged urban ethnic enclaves. The aim o… Show more

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Cited by 13 publications
(4 citation statements)
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“…Four common obesity risk factors (dietary consumption, physical activity, screen time, and sleep duration) were used to construct a healthy lifestyle score [ 43 , 44 ]. A healthy diet was based on four regularly consumed foods (meat, sugar-sweetened beverage, fruit, and vegetable consumption) linked to childhood obesity [ 45 , 46 ].…”
Section: Methodsmentioning
confidence: 99%
“…Four common obesity risk factors (dietary consumption, physical activity, screen time, and sleep duration) were used to construct a healthy lifestyle score [ 43 , 44 ]. A healthy diet was based on four regularly consumed foods (meat, sugar-sweetened beverage, fruit, and vegetable consumption) linked to childhood obesity [ 45 , 46 ].…”
Section: Methodsmentioning
confidence: 99%
“…Four main obesity-related factors (diet, physical activity, sleeping, and screen time) were used to calculate a healthy lifestyle score [25][26][27]. These obesity-related factors were collected from a baseline survey using a standardized questionnaire.…”
Section: Healthy Lifestyle Scorementioning
confidence: 99%
“…Third, our model is time invariant, and as such, does not account for the time it may take to observe changes in agents’ behavior due to policy expansions, or if effects change over time. Fourth, the weights assigned in the function used to predict the probability of selecting a given store to purchase food were partially informed by real data from the FRESH-Austin Study, from past studies in the same setting and population [ 51 , 68 , 69 , 70 ], and by expert opinion by the author team. While our model calibration suggests that these weights adequately reflect the value community members assign to different factors when selecting a food store, it is possible that in other settings or low-income populations the weight allocation would vary.…”
Section: Discussionmentioning
confidence: 99%