Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children’s weight status.Design: Cross-sectional survey of randomly selected households with children living in low-income diverse communities.Setting: A telephone survey conducted in 2009–2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child’s residence to food and physical activity outlets.Subjects: Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights.Results: Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children’s weight status.Conclusions: The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.

Disparities in healthy food access are well documented in cross-sectional studies in communities across the United States. However, longitudinal studies examining changes in food environments within various neighborhood contexts are scarce. In a sample of 142 census tracts in four low-income, high-minority cities in New Jersey, United States, we examined the availability of different types of food stores by census tract characteristics over time (2009–2017). Outlets were classified as supermarkets, small grocery stores, convenience stores, and pharmacies using multiple sources of data and a rigorous protocol. Census tracts were categorized by median household income and race/ethnicity of the population each year. Significant declines were observed in convenience store prevalence in lower- and medium-income and majority black tracts (p for trend: 0.004, 0.031, and 0.006 respectively), while a slight increase was observed in the prevalence of supermarkets in medium-income tracts (p for trend: 0.059). The decline in prevalence of convenience stores in lower-income and minority neighborhoods is likely attributable to declining incomes in these already poor communities. Compared to non-Hispanic neighborhoods, Hispanic communities had a higher prevalence of small groceries and convenience stores. This higher prevalence of smaller stores, coupled with shopping practices of Hispanic consumers, suggests that efforts to upgrade smaller stores in Hispanic communities may be more sustainable.

Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists.

Studies have examined the associations between emotions and overeating but have only rarely considered associations between emotions and specific food choices. The purpose of this secondary data analysis was to use mobile ecological momentary assessments (mEMAs) to examine associations between emotions and food choices among first-year college students living in residence halls. Using an intensive repeated-measures design, mEMAs were used to assess concurrent emotions and food choices in a racially/ethnically diverse sample of first-year college students (n = 663). Emotions were categorized as negative (sad, stressed, tired), positive (happy, energized, relaxed), and apathetic (bored, meh). Assessments were completed multiple times per day on four quasi-randomly selected days (three random weekdays and one random weekend day) during a 7-day period using random prompt times. Generalized estimating equations (GEE) were used to examine between- and within-person associations of emotional status with a variety of healthy and unhealthy food choices (sweets, salty snacks/fried foods, fruits/vegetables, pizza/fast food, sandwiches/wraps, meats/proteins, pasta/rice, cereals), adjusting for gender, day of week, and time of day, accounting for within-person dependencies among repeated measurements of eating behavior. At the between-person level, participants who reported positive emotions more frequently compared to others consumed meats/proteins more often (OR = 1.8; 99% CI = 1.2, 2.8). At the within-person level, on occasions when any negative emotion was reported (versus no negative emotion reported) participants were more likely to consume meats/proteins (OR = 1.5, 99% CI = 1.0, 2.1); on occasions when any positive emotion was reported as compared to occasions with no positive emotions, participants were more likely to consume sweets (OR = 1.7, 99% CI = 1.1, 2.6), but less likely to consume pizza/fast food (OR = 0.6, 99% CI = 0.4, 1.0). Negative and positive emotions were significantly associated with food choices. mEMA methodology provides a unique opportunity to examine these associations within and between people, providing insights for individual and population-level interventions. These findings can be used to guide future longitudinal studies and to develop and test interventions that encourage healthy food choices among first-year college students and ultimately reduce the risk of weight gain.

Lifestyle behaviors across the 24-h spectrum (i.e., sleep, sedentary, and active behaviors) drive metabolic risk. We describe the development and pro