This study explores the relationship between place-based social vulnerability and post-disaster migration in the U.S. Gulf Coast region following Hurricanes Katrina and Rita. Using county-level data from the U.S. Census Bureau, we develop a regional index of social vulnerability and examine how its various dimensions are related to migration patterns in the wake of the storms. Our results show that places characterized by greater proportions of disadvantaged populations, housing damage, and, to a lesser degree, more densely built environments were significantly more likely to experience outmigration following the hurricanes. Our results also show that these relationships were not spatially random, but rather exhibited significant geographic clustering. We conclude with a discussion of the implications of these findings for future research and public policy.

Overall, the E-Moms intervention was not able to decrease postpartum weight retention in women receiving WIC benefits compared to usual care received through the current WIC program. However, there is some evidence to suggest improved adherence to the intervention would improve weight management.

Objective: This study used spatial statistical methods to test the hypotheses that county-level adult obesity prevalence in the United States is (1) regionally concentrated at significant levels, and (2) linked to local-level factors, after controlling for state-level effects. Methods: Data were obtained from the Centers for Disease Control and Prevention and other secondary sources. The units of analysis were counties. The dependent variable was the age-adjusted percentage of adults who were obese in 2009 (body mass index >30 kg/m 2 ). Results: The prevalence of county-level obesity varied from 13.5% to 47.9% with a mean of 30.3%. Obesity prevalence across counties was not spatially random: 15.8% belonged to high-obesity regions and 13.5% belonged to low-obesity regions. Obesity was positively associated with unemployment, outpatient healthcare visits, physical inactivity, female-headed families, black populations, and less education. Obesity was negatively correlated with physician numbers, natural amenities, percent 65 years, Hispanic populations, and larger population size. A number of variables were notable for not reaching significance after controlling for other factors, including poverty and food environment measures.Conclusions: The findings demonstrate the importance of local-level factors in explaining geographic variation in obesity prevalence, and thus hold implications for geographically targeted interventions to combat the obesity epidemic.

Background Exercise is recommended for weight management, yet exercise produces less weight loss than expected, which is called weight compensation. The mechanisms for weight compensation are unclear. Objective The aim of this study was to identify the mechanisms responsible for compensation. Methods In a randomized controlled trial conducted at an academic research center, adults (n = 198) with overweight or obesity were randomized for 24 wk to a no-exercise control group or 1 of 2 supervised exercise groups: 8 kcal/kg of body weight/wk (KKW) or 20 KKW. Outcome assessment occurred at weeks 0 and 24. Energy intake, activity, and resting metabolic rate (RMR) were measured with doubly labeled water (DLW; with and without adjustments for change in RMR), armband accelerometers, and indirect calorimetry, respectively. Appetite and compensatory health beliefs were measured by self-report. Results A per-protocol analysis included 171 participants (72.5% women; mean ± SD baseline body mass index: 31.5 ± 4.7 kg/m2). Significant (P < 0.01) compensation occurred in the 8 KKW (mean: 1.5 kg; 95% CI: 0.9, 2.2 kg) and 20 KKW (mean: 2.7 kg; 95% CI: 2.0, 3.5 kg) groups, and compensation differed significantly between the exercise groups (P = 0.01). Energy intake by adjusted DLW increased significantly (P < 0.05) in the 8 KKW (mean: 90.7 kcal/d; 95% CI: 35.1, 146.4 kcal/d) and 20 KKW (mean: 123.6 kcal/d; 95% CI: 64.5, 182.7 kcal/d) groups compared with control (mean: −2.3 kcal/d; 95% CI: −58.0, 53.5 kcal/d). Results were similar without DLW adjustment. RMR and physical activity (excluding structured exercise) did not differentially change among the 3 groups. Participants with higher compared with lower compensation reported increased appetite ratings and beliefs that healthy behaviors can compensate for unhealthy behaviors. Furthermore, they increased craving for sweet foods, increased sleep disturbance, and had worsening bodily pain. Conclusions Compensation resulted from increased energy intake and concomitant increases in appetite, which can be treated with dietary or pharmacological interventions. Compensation was not due to activity or metabolic changes. This trial was registered at clinicaltrials.gov as NCT01264406.

Objective Significant clusters of high and low obesity counties have been demonstrated across the United States (U.S.). This study examined regional disparities in obesity prevalence and differences in the related structural characteristics across regions of the U.S. Design and Methods Drawing on model-based estimates from the Centers for Disease Control and Prevention, regional differences in county-level adult obesity prevalence (percent of the adult population [≥ 20 years] that was obese [BMI≥30kg/m2] within a county, 2009) were assessed with a LISA (Local Indicators of Spatial Association) analysis to identify geographic concentrations of high and low obesity levels. We utilized regional regime analysis to identify factors that were differentially associated with obesity prevalence between regions of the U.S. Results High and low obesity county clusters and the effect of a number of county-level characteristics on obesity prevalence differed significantly by region. These included the positive effect of African American populations in the South, the negative effect of Hispanic populations in the Northeast, and the positive effect of unemployed workers in the Midwest and West. Conclusions Our findings suggest the need for public health policies and interventions that account for different regional characteristics underlying obesity prevalence variation across the U.S.

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BackgroundInvestigating the association of the neighborhood social environment on physical activity is complex. A systematic scoping review was performed to (1) provide an inventory of studies assessing the influence of the neighborhood social environment on physical activity since 2006; (2) describe methodologies employed; and (3) formulate recommendations for the field.MethodsTwo databases were searched using terms related to ‘physical activity,’ ‘neighborhood,’ and ‘social environment’ in January 2017. Eligibility criteria included: 1) physical activity as an outcome; 2) neighborhood social environment as a predictor; 3) healthy population (without diagnosed clinical condition or special population); 4) observational or experimental design. Of 1352 studies identified, 181 were included. Textual data relevant to the social environment measurement and analysis were extracted from each article into qualitative software (MAXQDA) and coded to identify social environmental constructs, measurement methods, level of measurement (individual vs. aggregated to neighborhood), and whether authors explicitly recognized the construct as the social environment. The following measures were generated for each construct: number of unique measurements; % of times measured at an aggregate level; % of times authors referred to the construct as the social environment. Social environmental constructs were then grouped into larger descriptive dimensions.Results/findingsFifty-nine social environmental constructs were identified and grouped into 9 dimensions: Crime & Safety (n = 133 studies; included in 73% of studies); Economic & Social Disadvantage (n = 55, 33%); Social Cohesion & Capital (n = 47, 26%); Social Relationships (n = 22, 12%); Social Environment (n = 16, 9%); Disorder & Incivilities (n = 15, 8%); Sense of Place/Belonging (n = 8, 4%); Discrimination/Segregation (n = 3, 2%); Civic Participation & Engagement (n = 2, 1%). Across all articles, the social environment was measured using 176 different methods, was measured at an aggregate-level 38% of the time, and referred to as the social environment 23% of the time.ConclusionsInconsistent terminology, definitions, and measurement of the social environment and the lack of explicit language identifying constructs as the social environment make it challenging to compare results across studies and draw conclusions. Improvements are needed to increase our understanding of social environmental correlates and/or determinants of physical activity and facilitate cross-disciplinary conversations necessary to effectively intervene to promote physical activity.Trial registrationPROSPERO CRD42017059580.

BackgroundWeight loss induced only by exercise is frequently less than expected, possibly because of compensatory changes in energy intake and/or energy expenditure. The purpose of the Examination of Mechanisms (E-MECHANIC) of Exercise-Induced Weight Compensation trial is to examine whether increased energy intake and/or reduced spontaneous activity or energy expenditure (outside of structured exercise) account for the less than expected, exercise-associated weight loss.Methods/DesignE-MECHANIC is a three-arm, 6-month randomized (1:1:1) controlled trial. The two intervention arms are exercise doses that reflect current recommendations for (1) general health (8 kcal/kg body weight per week (8 KKW), about 900 kcal/wk) and (2) weight loss (20 KKW, about 2,250 kcal/wk). The third arm, a nonexercise control group, will receive health information only. The sample will include a combined total of 198sedentary, overweight or obese (body mass index: ≥25 kg/m2 to ≤45 kg/m2) men and women ages 18 to 65 years. The exercise dose will be supervised and tightly controlled in an exercise training laboratory. The primary outcome variables are energy intake, which will be measured using doubly labeled water (adjusted for change in energy stores) and laboratory-based food intake tests, and the discrepancy between expected weight loss and observed weight loss. Secondary outcomes include changes in resting metabolic rate (adjusted for change in body mass), activity levels (excluding structured exercise) and body composition. In an effort to guide the development of future interventions, the participants will be behaviorally phenotyped and defined as those who do compensate (that is, fail to lose the amount of weight expected) or do not compensate (that is, lose the amount of weight expected or more).DiscussionIn this study, we will attempt to identify underlying mechanisms to explain why exercise elicits less weight loss than expected. This information will guide the development of interventions to increase exercise-induced weight loss and maximize weight loss retention and related health benefits.Trial registrationClinicalTrials.gov ID: NCT01264406 (registration date: 20 December 2010).