SummarySmart growth is an approach to urban planning that provides a framework for making community development decisions. Despite its growing use, it is not known whether smart growth can impact physical activity. This review utilizes existing built environment research on factors that have been used in smart growth planning to determine whether they are associated with physical activity or body mass. Searching the MEDLINE, Psycinfo and Web-of-Knowledge databases, 204 articles were identified for descriptive review, and 44 for a more in-depth review of studies that evaluated four or more smart growth planning principles. Five smart growth factors (diverse housing types, mixed land use, housing density, compact development patterns and levels of open space) were associated with increased levels of physical activity, primarily walking. Associations with other forms of physical activity were less common. Results varied by gender and method of environmental assessment. Body mass was largely unaffected. This review suggests that several features of the built environment associated with smart growth planning may promote important forms of physical activity. Future smart growth community planning could focus more directly on health, and future research should explore whether combinations or a critical mass of smart growth features is associated with better population health outcomes.

Patients participating in a collaborative clinic-based food prescription program had a significant decrease in food insecurity.

Sleep, physical activity, and screen-time behaviors among adolescents are risk factors for physical health (eg, obesity), mental and emotional health, behavioral outcomes (eg, tobacco use), and performance-based outcomes (eg, academic achievement). 1-3 Accordingly, it is recommended that children (age 6-12 years) sleep 9 to 12 hours and adolescents (age 14-18 years) sleep 8 to 10 hours a night and that both groups accumulate at least 1 hour of moderate-intensity or vigorous

Eating out of the home has been positively associated with body weight, obesity, and poor diet quality. While cooking at home has declined steadily over the last several decades, the benefits of home cooking have gained attention in recent years and many healthy cooking projects have emerged around the United States. The purpose of this study was to develop an evidence-based conceptual framework of healthy cooking behavior in relation to chronic disease prevention. A systematic review of the literature was undertaken using broad search terms. Studies analyzing the impact of cooking behaviors across a range of disciplines were included. Experts in the field reviewed the resulting constructs in a small focus group. The model was developed from the extant literature on the subject with 59 studies informing 5 individual constructs (frequency, techniques and methods, minimal usage, flavoring, and ingredient additions/replacements), further defined by a series of individual behaviors. Face validity of these constructs was supported by the focus group. A validated conceptual model is a significant step toward better understanding the relationship between cooking, disease and disease prevention and may serve as a base for future assessment tools and curricula.

Background: There is consensus that development and evaluation of a systems-oriented approach for child obesity prevention and treatment that includes both primary and secondary prevention efforts is needed. This article describes the study design and baseline data from the Texas Childhood Obesity Research Demonstration (TX CORD) project, which addresses child obesity among lowincome, ethnically diverse overweight and obese children, ages 2-12 years; a two-tiered systems-oriented approach is hypothesized to reduce BMI z-scores, compared to primary prevention alone.Methods: Our study aims are to: (1) implement and evaluate a primary obesity prevention program; (2) implement and evaluate efficacy of a 12-month family-centered secondary obesity prevention program embedded within primary prevention; and (3) quantify the incremental cost-effectiveness of the secondary prevention program. Baseline demographic and behavioral data for the primary prevention community areas are presented.Results: Baseline data from preschool centers, elementary schools, and clinics indicate that most demographic variables are similar between intervention and comparison communities. Most families are low income ( £ $25,000) and Hispanic/Latino (73.3-83.8%). The majority of parents were born outside of the United States. Child obesity rates exceed national values, ranging from 19.0% in preschool to 35.2% in fifth-grade children. Most parents report that their children consume sugary beverages, have a television in the bedroom, and do not consume adequate amounts of fruits and vegetables.

Sedentary behavior is emerging as an independent risk factor for pediatric obesity. Some evidence suggests that limiting sedentary behavior alone could be effective in reducing body mass index (BMI) in children. However, whether adding physical activity and diet-focused components to sedentary behavior reduction interventions could lead to an additive effect is unclear. This meta-analysis aims to assess the overall effect size of sedentary behavior interventions on BMI reduction, and to compare whether interventions that have multiple components (sedentary behavior, physical activity, and diet) have a higher mean effect size than interventions with single (sedentary behavior) component. Included studies (N=25) were randomized controlled trails of children (<18 years) with intervention components aimed to reduce sedentary behavior and measured BMI at pre- and post-intervention. Effect size was calculated as the mean difference in BMI change between children in an intervention and a control group. Results indicated that sedentary behavior interventions had a significant effect on BMI reduction. The pooled effect sizes of multi-components interventions (g=−.060~−.089) did not differ from the single-component interventions (g=−.154), and neither of them had a significant effect size on its own. Future pediatric obesity interventions may consider focusing on developing strategies to decrease multiple screen-related sedentary behaviors.

MEND/CATCH6-12 was more efficacious for BMI reduction at 3 months but not 12 months compared to Next Steps in underserved children. Intervention compliance influenced outcomes, emphasizing the need for research in sustaining family engagement in low-income populations.

IntroductionStatistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model.MethodsA Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations.ResultsIn general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified.ConclusionsRoutinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.