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Background Investigating 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. Methods Two 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/findings Fifty-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. Conclusions Inconsistent 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.

Objective Food cravings are thought to be the result of conditioning or pairing hunger with consumption of certain foods. Methods In a two-year weight loss trial, subjects were randomized to one of four diets that varied in macronutrient content. The Food Craving Inventory (FCI) was used to measure cravings at baseline, 6, and 24 months. Also, food intake was measured at those time points. To measure free-living consumption of food items measured in the FCI, items on the FCI were matched to the foods consumed from the food intake assessments. Secondarily, we analyzed the amount of food consumed on food intake assessments from foods on the FCI. Results 367 subjects who were overweight and obese were included. There was an association between change from baseline FCI item consumption and change in cravings at months 6 (p

Additional file 1. PRISMA 2009 Checklist.

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.