Environmental and Lifestyle Factors Associated with Overweight and Obesity in Perth, Australia

Body Mass Index Changes at 1.5 and 3 Years of Age Affect Adult Body Composition

Early childhood lays the foundation for many outcomes in later life. Recent studies suggest that early childhood development may contribute to lifestyle-related diseases such as obesity, type 2 diabetes, and cardiovascular disease in adulthood; however, there have been few investigations on this association among adults in Japan. Therefore, we examined the relationship between recent physical measurements in adults who underwent health checkups at our university and their physical measurements at birth and during infancy recorded in the Maternal and Child Health Handbook. The median age and body mass index (BMI) of the participants were 36 years and 20.4 kg/m2, respectively. BMI at the time of health checkup in adults did not correlate with physical measurements at birth, but it was found to be associated with BMI at 1.5 (regression coefficient (β) 0.53, p < 0.05) and 3 (β 0.7, p < 0.01) years of age. In addition, the waist-to-height ratio in adulthood was also associated with BMI at 1.5 (β 0.01, p < 0.05) and 3 (β 0.01, p < 0.05) years of age. These findings suggest that it is critical to provide appropriate guidance to children with high BMI and their parents during health checkups to prevent lifestyle-related disorders in adulthood.

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning

Purpose: With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. Methods: A total of 12,712 participants, excluding individuals under 20, were selected from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. The mean age of the sample was 46.86 years (±16.97), with a gender distribution of 6,721 males and 5,991 females. The variables included demographic, physical-related variables, and lifestyle variables. This study developed 42 prediction models using six machine learning methods, including logistic regression, Support Vector Machine (SVM), decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The relative importance of each variable was evaluated by permutation feature importance. Results: The results illustrated that the LightGBM was the most effective algorithm for predicting recreational activity participation (accuracy: .838, precision: .783, recall: .967, F1-score: .865, AUC: .826). In particular, prediction performance increased when the demographic and lifestyle datasets were used together. Next, as the result of the permutation feature importance based on the top models, education level and moderate-vigorous physical activity (MVPA) were found to be essential variables. Conclusion: These findings demonstrated the potential of a data-driven approach utilizing machine learning in a recreational discipline. Furthermore, this study interpreted the prediction model through feature importance analysis to overcome the limitation of machine learning interpretability.

Metrics for weight management success: an examination of the lifestyle score

Multiple weight loss attempts, and weight regain are common. Various lifestyle changes are often required for success, but just diet and exercise are often promoted. The amount of lifestyle changes needed for successful weight management is still unclear. Lifestyle metrics, such as the Lifestyle Score (LS) of The Lifestyle Questionnaire for Weight Management (LQ-WM), may provide clarification and predict success rates. The range of possible scores on this measure is from -49 to 77, where higher scores suggest healthier lifestyles. Hypotheses are that higher lifestyle scores will be found with successful weight management outcomes. Method: 1,007 online participants completed the LQ-WM and other items related to weight management. Results: 90.7% (39/43) of participants with an LS of 50 + reported successful weight loss, while just 5% (2/40) reported weight regain. 35.7% (20/56) of those with LS below zero reported weight loss success, and 39.4% (13/33) reported weight regain. Significant (p < .001) mean LS differences existed between those with healthy BMI versus the obese range (m = 22.2, SD = 15.51; m = 6.70, SD = 15.97) and those reporting success versus those not reporting success (m = 26.92, SD = 17.03; m = 13.68, SD = 14.95). Discussion and Conclusions: Future studies should employ more diverse samples and use causal designs. The LS from the LQ-WM may be a helpful tool in research and clinical interactions to raise patients' awareness of behavioral patterns, increase the likelihood of success, and prevent future weight gain.

The sound of safety: exploring the determinants of prevention intention in noisy industrial workplaces

Occupational noise exposure is a pervasive issue in many industries, leading to a range of health issues and sleep disturbances among workers. Additionally, there is a strong desire among these workers to prevent industrial accidents. This study, aimed at enhancing worker health and well-being, utilized a survey distributed by the Korean Confederation of Trade Unions to field workers. Data from 1285 workers were collected and analyzed using partial least squares structural equation modeling (PLS-SEM) to identify and understand the factors affecting prevention intention in noisy work environments. Our findings indicate that health problems resulting from occupational noise exposure significantly influence insomnia, perceived severity of potential accidents, perceived benefits of preventive measures, and perceived barriers. Perceived severity was significantly correlated with prevention intention, emphasizing the role of risk perception in motivating preventive behaviors. Perceived benefits were also significantly associated with prevention intention, highlighting the importance of positive outcomes in influencing workers' behaviors. Additionally, perceived barriers showed a significant relationship with prevention intention, suggesting that overcoming these barriers is crucial in promoting preventive behaviors. Demographic factors such as gender displayed a significant association with prevention intention, while age did not. This study provides valuable insights into the multifaceted factors influencing workers' intention to prevent industrial accidents in noisy environments, underlining the importance of comprehensive data collection tools in understanding these dynamics.

Associations between older adults' going-out frequency and land price of neighbourhoods: Potency of land price as an indicator of homebound tendency

The aim of this study is to identify the potency of land price as a general indicator of the homebound tendency of neighbourhoods. For this purpose, we focus on the associations between land price and older adults' going-out frequency, while considering the similarities and differences from factors associated with walkability. The study targets are 19,270 individuals living in the Tokyo metropolitan area who are aged 65 years or older who are not certified as needing public long-term care insurance. This study uses a two-step procedure: a) the land price of each neighbourhood is estimated using rent price data from 2010 to 2019 collected by an official Japanese realtor organization; b) the associations between older adults' goingout frequency, according to the 2016 Japan Gerontological Evaluation Study, and the estimated land price of their neighbourhoods is identified using multilevel analysis. The results indicate that land price has strong negative associations with older adults' going-out frequency. Associations between land price or other walkability-associated factors, such as population density, ratio of commercial area, and proximity to the nearest train station, only appear when these factors and land price are included within the same model. The results suggest that, when inspecting homebound tendency in some neighbourhoods, factors relating to the built environment must be considered alongside land prices. Furthermore, the homebound status of older adults is a pressing challenge, especially in neighbourhoods with high land prices.

Integrating street view images and deep learning to explore the association between human perceptions of the built environment and cardiovascular disease in older adults

Understanding how built environment attributes affect health remains important. While many studies have explored the objective characteristics of built environments that affect health outcomes, few have examined the role of human perceptions of built environments on physical health. Baidu Street View images and computer vision technological advances have helped researchers overcome the constraints of traditional methods of measuring human perceptions (e.g., these methods are laborious, time-consuming, and costly), allowing for large-scale measurements of human perceptions. This study estimated human perceptions of the built environment (e.g., beauty, boredom, depression, safety, vitality, and wealth) by adopting Baidu Street View images and deep learning algorithms. Negative binomial regression models were employed to analyze the relationship between human perceptions and cardiovascular disease in older adults (e.g., ischemic heart disease and cerebrovascular disease). The results indicated that wealth perception is negatively related to the risk of cardiovascular disease. However, depression and vitality perceptions are positively associated with the risk of cardiovascular disease. Furthermore, we found no relationship between beauty, boredom, safety perceptions, and the risk of cardiovascular disease. Our findings highlight the importance of human perceptions in the development of healthy city planning and facilitate a comprehensive understanding of the relationship between built environment characteristics and health outcomes in older adults. They also demonstrate that street view images have the potential to provide insights into this complicated issue, assisting in the formulation of refined interventions and health policies.

Understanding cross-data dynamics of individual and social/environmental factors through a public health lens: explainable machine learning approaches

Introduction

The rising prevalence of obesity has become a public health concern, requiring efficient and comprehensive prevention strategies.

Methods

This study innovatively investigated the combined influence of individual and social/environmental factors on obesity within the urban landscape of Seoul, by employing advanced machine learning approaches. We collected 'Community Health Surveys' and credit card usage data to represent individual factors. In parallel, we utilized 'Seoul Open Data' to encapsulate social/environmental factors contributing to obesity. A Random Forest model was used to predict obesity based on individual factors. The model was further subjected to Shapley Additive Explanations (SHAP) algorithms to determine each factor's relative importance in obesity prediction. For social/environmental factors, we used the Geographically Weighted Least Absolute Shrinkage and Selection Operator (GWLASSO) to calculate the regression coefficients.

Results

The Random Forest model predicted obesity with an accuracy of >90%. The SHAP revealed diverse influential individual obesity-related factors in each Gu district, although 'self-awareness of obesity', 'weight control experience', and 'high blood pressure experience' were among the top five influential factors across all Gu districts. The GWLASSO indicated variations in regression coefficients between social/environmental factors across different districts.

Conclusion

Our findings provide valuable insights for designing targeted obesity prevention programs that integrate different individual and social/environmental factors within the context of urban design, even within the same city. This study enhances the efficient development and application of explainable machine learning in devising urban health strategies. We recommend that each autonomous district consider these differential influential factors in designing their budget plans to tackle obesity effectively.

Factors affecting decreased physical activity during the COVID-19 pandemic: an age-, gender-, and body mass index-matched study

Purpose

This study aims to investigate the association between factors affecting decreased physical activity (PA) during the COVID-19 pandemic by matching groups based on age, gender, and BMI variables using public Community Health Survey (CHS) data.

Methods

Data from the CHS was selected and used to investigate health-related factors related to PA, including demographic, psychological, behavioral characteristics, sociocultural, and chronic disease. Exact group matching was conducted based on age, gender, and BMI variables. Frequency analysis, Chi-square test (χ2 test), and multinominal logistic regression analysis were performed to analyze the data, and odds ratio (OR) and 95% confidence interval (95% CI) were presented. The study also examined the impact of COVID-19 on PA, the fear of PA infection.

Results

The logistic regression analysis by gender showed that PA decreased in all age groups, males, and females during the COVID-19 pandemic. The decrease in PA was lower in age groups other than those aged 60 or older. Stress experience, residence area, housing type, drinking, smoking, education level, and fear of infection were found to affect decreased PA due to COVID-19. Specifically, experiencing stress (Odds Ratio [OR] = 1.178; 95% Confidence Interval [CI] = 1.054 ~ 1.317) and increased smoking (OR = 1.332; 95% CI = 1.073 ~ 1.653) had a slightly higher impact on decreased PA. Conversely, living in a suburban area (OR = 0.653; CI = 0.585 ~ 0.728), having public housing (OR = 0.836; CI = 0.754 ~ 0.928), having less than a high school education (OR = 0.813; CI = 0.729 ~ 0.907), staying the same with alcohol (OR = 0.567; CI = 0.507 ~ 0.633) and smoking (OR = 0.836; CI = 0.728 ~ 0.959), and having low fear of infection (OR = 0.817; CI = 0.737 ~ 0.905) had a slightly lower impact on decreased PA.

Conclusion

PA should be maintained or increased, particularly in the context of social distancing measures during the pandemic. To ensure that PA can be sustained, a program should be developed that considers the individual's geographical location, economic status, lifestyle, and environment.

An investigation of body mass distributional changes in Australia, 1995-2017/18

This study investigates changes in the distribution of body mass for adult Australians between 1995 and 2017/18. Using three nationally representative health surveys, we first apply the parametric generalised entropy (GE) class of inequality indices to measure the level of disparity in the body mass distribution. Results from the GE measure reveal that, while growth of body mass inequality is a population-wide experience, demographic and socio-economic factors explain only a modest portion of total inequality. We then apply the relative distributions (RD) method to garner richer insights on changes to the body mass distribution. The non-parametric RD method reveals growth in the proportion of adult Australians falling into the upper deciles of the body mass distribution since 1995. Then, hypothetically keeping the shape of the distribution unchanged, we discern that body mass increases across all deciles of the distribution (location effect) is an important contributor to the observed distributional change. After removing the location effect, however, we find a non-trivial role for distributional shape changes (growth of the proportion of adults at the upper and lower parts of the distribution as the proportion in the middle diminish). While our findings support current policy directions that target the population as a whole, factors driving shape changes to the body mass distribution also need consideration when designing anti-obesity campaigns, especially when aimed at women.

Sedentary lifestyle with increased risk of obesity in urban adult academic professionals: an epidemiological study in West Bengal, India

Ectopic fat deposition is more strongly associated with obesity-related health problems including type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), hypothyroidism, arthritis, etc. Our study aimed at identifying the cumulative role of several risk factors in developing obesity and the role of ectopic fat (visceral fat) in predicting cardiovascular disease risk in varied age groups among urban adult academic professionals in West Bengal. 650 adults (Male = 456; Female = 194) associated with the academic job (age 20-65 years) in urban West Bengal were randomly selected for anthropometric, blood biochemical, and questionnaire-based analyses. Body Mass Index and Visceral Fat% exhibited comparable association with all the other anthropometric parameters (e.g. Whole body Subcutaneous fat%: male-Linear Regression Comparison: F = 11.68; P < 0.001; female-F = 6.11; P < 0.01). Therefore, VF% acts as a risk factor alongside BMI in instances where BMI fails alone. The presence of T2DM, hypertension, and hypothyroidism in the case groups confirmed their obesity-associated longitudinal pattern of inheritance. Unhealthy diet pattern indicates improper liver function, vitamin D deficiency, and increased erythrocytic inflammation. An overall sedentary lifestyle with parental history of obesity was found to be significant in the longitudinal transmission of the disease.

Enhanced green view index

Quantifying street-level greenery has been the subject of interest for researchers as it has several implications for community residents. Green View Index (GVI) is a widely used parameter to compute the greenery along the streets. However, it does not account for the health of the greenery. The new Enhanced Green View Index (EGVI) that we propose computes the amount of greenery along the streets along with the health of the greenery.

• The new indicator computes street-level greenery;

• Considers the health of vegetation while calculating greenery; and

• Helps to study the impact of street-level greenery on community residents precisely.

Drivers to Obesity-A Study of the Association between Time Spent Commuting Daily and Obesity in the Nepean Blue Mountains Area

Obesity has become a public health challenge in every country on this planet, with a substantial contribution to global mortality and morbidity. Studies of the built environment have shown some promise in understanding the drivers of this obesity pandemic. This paper contributes to this knowledge, by focusing on one aspect of the urban environment and asking whether there is an association between commuting and obesity in residents of the Nepean Blue Mountains area on the fringes of Sydney. This is a cross-sectional study with obesity being the dependent variable, and commuting the independent variable, where 45 min or less was defined as local and distant commute was more than 45 min. In the sample of 158 respondents, the risk of obesity was twice as likely in the distant commuters than in the local commuters (OR 2.04, 95% CI 1.051 to 3.962, p = 0.034). Investigation of possible mediators of this association was limited by sample size; however, mode of transport was found to be a significant mediator. The results support the design of cities to provide health supporting environments for all residents, including equitable access to employment at a reasonable distance and effective public transport.

Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study

BACKGROUND

Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks.

OBJECTIVE

We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments.

METHODS

We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data.

RESULTS

We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of people (55,150/229,755) moved within King County at least once during the follow-up, representing 84,698 total moves. Members tended to move to new neighborhoods matching their origin neighborhoods on residential density and property values.

CONCLUSIONS

Data were available in the KPWA database to construct a very large cohort based in King County, Washington. Future analyses will directly examine associations between neighborhood conditions and longitudinal changes in body weight and diabetes as well as other health conditions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16787.

Morphology of Urban Villages in China: A Case Study of Dayuan Village in Guangzhou

Urban villages play an important role in providing affordable housing to urban migrants in Chinese cities. They are considered as supplementary to the dual rural-urban system in China. Of central importance to studying urban villages is how the morphology of these informal settlements affects urban life. It is essential for urban planners and designers to examine the morphology of urban villages. This paper, therefore, investigates the morphology of urban villages using the case study of Dayuan Village in Guangzhou, China. The morphology of this urban village is tested against four main elements of urban morphology: urban density, accessibility, functional mix, and urban interface. Our results revealed that the type of street within the urban village has considerable influence on accessibility, functional mix, and urban interface. Regarding urban density, our results show that buildings’ height is not influenced by the centrality of buildings nor land value; however, it is likely that it is affected by planning agreements between the village committee and the local government. Land coverage does not comply with the planning regulation for residential districts. Regarding accessibility analysis, the number of entrances to streets is influenced by the type of street under analysis. The distribution of different types of functional mix is also affected by the type of street within the urban village. The buildings with a mix of ‘live/visit’ are concentrated along the formal streets and primary inner streets. The mono-functional use of ‘live’ and the bi-functional mix of ‘live/work’ are mostly located in the secondary inner streets. Regarding urban interface, our results demonstrate that the formal streets have an interface with considerable porosity, and that this can contribute to the livelihood of the immediate area.

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

This study investigates the impact of neighborhood-built environments on obesity in interrelationship with socioeconomic status (SES)—controlling for dietary patterns and physical activities of residents—using structural equation modeling (SEM). A total of 577 samples who are between 19 and 64 years old and reside in Seoul are extracted from Korea National Health and Nutrition Examination Survey (KNHNES), 2015. Neighborhood environments are represented as the two latent constructs—walkability and leisure amenities—composited with indicators such as density of intersections, density of mixed-use area, and the area of open spaces and are aggregated by jurisdictional unit in Seoul. We found that greater walkability in a neighborhood explained a lower body mass index (BMI) among residents, whereas more urban leisure amenities in a neighborhood explained a higher BMI. The finding suggests that a walking-friendly environment is more effective than active recreational amenities in inducing people to engage in daily physical activities to the level that reduces obesity rate. SES exerted a negative impact on BMI of a greater magnitude than the impact of either of the environmental living conditions, reinforcing the importance of general wealth and education level in leading to a healthy lifestyle. Our research contributes to growing evidence of a relationship between obesity and the built environment in the context of Asian countries where the prevalence of obesity is becoming a serious issue and requires immediate attention.

Developing a Health-Promotion Program Based on the Action Research Paradigm to Reduce Cardiovascular Disease Risk Factors among Blue Collar Workers

This study developed and evaluated a health management program based on the participant-centered concept of action research to reduce cardiovascular disease (CVD) risk factors among blue collar workers. Data from structured questionnaires completed by 32 workers in a small-to-medium sized workplace from September 2015 to October 2016 as well as participants’ anthropometrical (weight and waist) and biological (blood pressure, glucose, total cholesterol, triglyceride, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol) data were analyzed using paired t-test and Fisher’s exact test. To examine the longitudinal effect of the intervention, survival analysis and linear mixed model (LMM) were used. There was an improvement in participants’ self-regulation in maintaining health-promoting behaviors, body weight, blood pressure, and HDL cholesterol following the intervention. Furthermore, the effects of the health management program continued even after the program ended. These findings suggest that the health management program developed in this study could be effective in reducing CVD risk factors among workers in small-to-medium sized workplaces and should be applied to other small-to-medium sized workplaces to foster health-promoting behaviors.

Use of Social Media, Search Queries, and Demographic Data to Assess Obesity Prevalence in the United States

Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter's streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. Additionally, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.789 (95% CI, 0.785, 0.786) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

Cross-sectional analysis of physical activity in 2-4-year-olds in England with paediatric quality of life and family expenditure on physical activity

BACKGROUND

Many children do not meet the recommended level of daily physical activity, even with the widely acknowledged health benefits associated with being physically active. There is a need to establish factors related to physical activity in children so that public health interventions may be appropriately designed. We investigated the association between Pediatric Quality of Life Inventory (PedsQL), family expenditure on physical activity and objectively measured daily physical activity in 2-4-year-old children.

METHODS

Cross-sectional study with a sample of 81 UK preschool children taking part in the NAPSACC UK feasibility randomized controlled trial. Descriptive statistics are presented. We undertook Student t-tests to establish differences in physical activity by gender, age, parental education and nursery versus non-nursery days. Mixed effects linear regressions were used to model the association between minutes spent physically activity, minutes spent in moderate-to-vigorous (MVPA) physical activity and PedsQL scores (physical and psychosocial) and family expenditure on physical activity.

RESULTS

Most children (88.9%) did not engage in the recommended 180 min daily physical activity. There was mean (SD) of 141.9 (33.1) daily minutes of physically activity and 22.2 min per day (SD = 9.9) of MVPA. Boys and older children were more physically active. Children were more active on nursery days. There was no difference in physical activity by parental education. Half of the sample parents (50.6%) spent less than £9.00 weekly on their pre-schooler's physical activity. Children within the highest tertile of PedsQL physical functioning scores had higher levels of MVPA (3.6, 95% CI: - 1.3-8.4, p-value 0.15), although confidence intervals crossed the null in the adjusted model. We found no evidence of an association between positive PedsQL psychosocial scores, or higher parental expenditure on physical activity, with the physical activity variables.

CONCLUSIONS

Children in this sample were not meeting the recommended 180 min of daily physical activity. The 2-4-year-olds were most active on nursery days. There is no evidence of an association between better PedsQL physical scores and higher levels of MVPA. There was no evidence of an association between expenditure on physical activity and time spent physically active. Further examination in larger representative datasets is needed.

A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning

We develop a method based on computer vision and a hierarchical multilevel model to derive an Urban Street Tree Vegetation Index which aims to quantify the amount of vegetation visible from the point of view of a pedestrian. Our approach unfolds in two steps. First, areas of vegetation are detected within street-level imagery using a state-of-the-art deep neural network model. Second, information is combined from several images to derive an aggregated indicator at the area level using a hierarchical multilevel model. The comparative performance of our proposed approach is demonstrated against a widely used image segmentation technique based on a pre-labelled dataset. The approach is deployed to a real-world scenario for the city of Cardiff, Wales, using Google Street View imagery. Based on more than 200,000 street-level images, an urban tree street-level indicator is derived to measure the spatial distribution of tree cover, accounting for the presence of obstructing objects present in images at the Lower Layer Super Output Area (LSOA) level, corresponding to the most commonly used administrative areas for policy-making in the United Kingdom. The results show a high degree of correspondence between our tree street-level score and aerial tree cover estimates. They also evidence more accurate estimates at a pedestrian perspective from our tree score by more appropriately capturing tree cover in areas with large burial, woodland, formal open and informal open spaces where shallow trees are abundant, in high density residential areas with backyard trees, and along street networks with high density of high trees. The proposed approach is scalable and automatable. It can be applied to cities across the world and provides robust estimates of urban trees to advance our understanding of the link between mental health, well-being, green space and air pollution.

Influence of the type of occupation on osteoarthritis of the knee in men: The Korean National Health and Nutrition Examination Survey 2010-2012

Objectives

We investigated the influence of different types of lifetime occupation on knee osteoarthritis (OA) and pain based on a nationwide survey in Korea.

Methods

We conducted a cross‐sectional study using Korea National Health and Nutrition Examination Survey (KNHANES) from 2010 to 2012. Men ≥50 years old were divided into four occupational groups: white‐collar (WC) workers, pink‐collar (PC) workers, blue‐collar (BC) workers, and agribusiness and low‐level (AL) workers. The risks for knee OA and pain development according to occupation were compared to the risks in WC workers, using multiple logistic regression analyses.

Results

Different occupations were associated with different levels of risk for knee OA and knee pain. The risk for knee OA was higher in BC workers and in AL workers than in WC workers, in order of higher risk. The risk for severe knee OA was likewise higher in AL workers and in BC workers, in order of higher risk. The risk for chronic knee pain was higher in AL workers, BC workers, and PC workers, in order of higher risk. The risk for moderate to severe chronic knee pain was higher in AL workers and BC workers, in order of higher risk.

Conclusions

The results suggest that occupation might affect the development of knee OA and presence of chronic knee pain in men ≥50 years old. WC workers are least affected by knee OA and chronic knee pain, and AL and BC workers are at clearly higher risk for knee OA and chronic knee pain.

C. P, Kuikel J, Newell J, Peters R, Prestwich A, King R. Developing a socio-ecological model of dietary behaviour for people living with diabetes or high blood glucose levels in urban Nepal: A qualitative investigation

Instances of non-communicable diseases such as diabetes are on the rise globally leading to greater morbidity and mortality, with the greatest burden in low and middle income countries [LMIC]. A major contributing factor to diabetes is unhealthy dietary behaviour. We conducted 38 semi structured interviews with patients, health professionals, policy-makers and researchers in Kathmandu, Nepal, to better understand the determinants of dietary behaviour amongst patients with diabetes and high blood glucose levels. We created a social ecological model which is specific to socio-cultural context with our findings with the aim of informing culturally appropriate dietary behaviour interventions for improving dietary behaviour. Our findings show that the most influential determinants of dietary behaviour include cultural practices (gender roles relating to cooking), social support (from family and friends), the political and physical environment (political will, healthy food availability) and individuals' motivations and capabilities. Using these most influential determinants, we suggest potentially effective dietary interventions that could be implemented by policy makers. Our findings emphasise the importance of considering socio-cultural context in developing interventions and challenges one-size-fits-all approaches which are often encouraged by global guidelines. We demonstrate how multifaceted and multi layered models of behavioural influence can be used to develop policy and practice with the aim of reducing mortality and morbidity from diabetes.

Spatial Accessibility of Urban Forests in the Pearl River Delta (PRD), China

The Pearl River Delta (PRD) is one of the most important economic zones both in China and in the world. Its rapid economic development has been associated with many environmental problems such as the loss of forests in urban areas. We estimated the accessibility of forests in the PRD by quantifying spatial proximity and travel time. We found that distances from a large proportion of the points of interest (POIs) (~45%) and urban lands (~38%, where ~49 urban residents live) to the nearest forests were greater than 1000 m; suggesting a low spatial proximity to forests. Urban parks—important outdoor recreational areas—appeared to have insufficient forest coverage within their 1000 m buffer zones. When forest accessibility was measured by travel time under optimal modes of transport; it was less than 15 min for most urban lands (~95%), which accommodates 98% of the total urban population. More importantly; the travel time to the nearest forest was negatively correlated with gross domestic product density (GDPd), but not with population density (POPd). The GDPd and POPd; however; increased log-linearly with the Euclidean distance to the nearest forest. In addition to the low proximity to forests; there existed inequalities among urban residents who live in areas with different levels of GDPd and POPd. Future urban planning needs not only to increase the total coverage of urban forests; but also to improve their spatial evenness across the urban landscapes in the PRD.

The Moderating Role of Social Neighbourhood Factors in the Association between Features of the Physical Neighbourhood Environment and Weight Status

BACKGROUND

This paper investigated the independent and joint associations between aspects of the physical neighbourhood environment and social neighbourhood factors with BMI and overweight status in European adults.

METHODS

Data from 5,199 participants in the SPOTLIGHT survey were analysed. Participants reported on their height, weight and perceptions of the neighbourhood. Objectively measured aspects of the physical neighbourhood environment included: presence of recreational facilities, features of the active transportation environment, neighbourhood aesthetics and presence of different types of food outlets. Social factors included the self-reported variables social network, social cohesion, social trust and perceived crime and the census variable neighbourhood socioeconomic status. Outcome measures were BMI and overweight status. Main associations between physical and social factors and BMI/overweight status were analysed using multilevel regression analyses adjusted for confounders. Moderation analysis was conducted by adding the interaction terms between physical and social neighbourhood factors one by one to the multivariable models. Significant interaction terms were then stratified.

RESULTS

Significant associations with BMI/overweight status were found for features of the active transportation environment and all social factors, except perceived crime. Several significant interaction terms were detected, but no significant associations between the physical neighbourhood environment and BMI/overweight status were found after stratification.

CONCLUSION

We did not find consistent interactions between physical and social neighbourhood factors to explain BMI and overweight status.

High relative abundance of firmicutes and increased TNF-α levels correlate with obesity in children

OBJECTIVE

To investigate the correlation among pro- or anti-inflammatory cytokines and the two main gut microbiota phyla in obese children.

MATERIALS AND METHODS

Anthropometric data were obtained from 890 children under 14 years old to determine the degree of obesity. Serum cytokine concentration was measured by ELISA. Relative abundance of gut microbiota in feces was evaluated by quantitative RealTime PCR assays.

RESULTS

Anthropometric and biochemical parameters were statistically higher in overweigth/ obese children (OW/O) than in lean (NW), Increased TNF-α levels were found in obese children that also have a high relative abundance of Firmicutes.

CONCLUSIONS

Obese children have a high relative abundance of Firmicutes that correlates with increased levels of TNF-α. This is the first study that shows a relation between Firmicute abundance and TNF-α serum concentration in obese children.

Neighborhood Environment Measurements and Anthropometric Indicators of Obesity: Results from the Women and Their Children's Health (WaTCH) Study

We compared geographic information system (GIS)- and Census-based approaches for measuring the physical and social neighborhood environment at the census tract-level versus and audit approach on associations with body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR). Data were used from the 2012-2014 Women and Their Children's Health (WaTCH) Study (n=940). Generalized linear models were used to obtain odds ratios (ORs) for BMI (≥30 kg/m²), WC (>88 cm), and WHR (>0.85). Using an audit approach, more adverse neighborhood characteristics were associated with a higher odds of WC (OR: 1.10; 95% CI: 1.05, 1.15) and WHR (OR: 1.09; 95% CI: 1.05, 1.14) after adjustment for age, race/ethnicity, income, and oil spill exposure. There were no significant associations between GIS- and Census- based measures with obesity in adjusted models. Quality aspects of the neighborhood environment captured by audits at the individual-level may be more relevant to obesity than physical or social aspects at the census-tract level.

Linking profiles of neighborhood elements to health and related outcomes among children across the United States

The current study explored association of neighborhood elements to children's health and related outcomes. Nationally representative data (N = 49,513,974, ages 6-17, 51.1% Male) was used to empirically define classes of neighborhoods based on presence or absence of various neighborhood elements. Analyses resulted in a three-class model: 1) "High Assets, Low Disorganization" (64.57%), 2) "High Assets, High Disorganization" (13.51%), and 3) "Few Assets, Low Disorganization" (21.91%). Class Membership was differentially associated with health, flourishing, and neighborhood cohesion. Results suggest health interventions should focus on increasing neighborhood assets, decreasing levels of neighborhood violence and poverty, and improving social dynamics of neighborhoods.

Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity

IMPORTANCE

More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as genetics, diet, physical activity, and the environment. However, evidence indicating associations between the built environment and obesity has varied across studies and geographical contexts.

OBJECTIVE

To propose an approach for consistent measurement of the features of the built environment (ie, both natural and modified elements of the physical environment) and its association with obesity prevalence to allow for comparison across studies.

DESIGN

The cross-sectional study was conducted from February 14 through October 31, 2017. A convolutional neural network, a deep learning approach, was applied to approximately 150 000 high-resolution satellite images from Google Static Maps API (application programing interface) to extract features of the built environment in Los Angeles, California; Memphis, Tennessee; San Antonio, Texas; and Seattle (representing Seattle, Tacoma, and Bellevue), Washington. Data on adult obesity prevalence were obtained from the Centers for Disease Control and Prevention's 500 Cities project. Regression models were used to quantify the association between the features and obesity prevalence across census tracts.

MAIN OUTCOMES AND MEASURES

Model-estimated obesity prevalence (obesity defined as body mass index ≥30, calculated as weight in kilograms divided by height in meters squared) based on built environment information.

RESULTS

The study included 1695 census tracts in 6 cities. The age-adjusted obesity prevalence was 18.8% (95% CI, 18.6%-18.9%) for Bellevue, 22.4% (95% CI, 22.3%-22.5%) for Seattle, 30.8% (95% CI, 30.6%-31.0%) for Tacoma, 26.7% (95% CI, 26.7%-26.8%) for Los Angeles, 36.3% (95% CI, 36.2%-36.5%) for Memphis, and 32.9% (95% CI, 32.8%-32.9%) for San Antonio. Features of the built environment explained 64.8% (root mean square error [RMSE], 4.3) of the variation in obesity prevalence across all census tracts. Individually, the variation explained was 55.8% (RMSE, 3.2) for Seattle (213 census tracts), 56.1% (RMSE, 4.2) for Los Angeles (993 census tracts), 73.3% (RMSE, 4.5) for Memphis (178 census tracts), and 61.5% (RMSE, 3.5) for San Antonio (311 census tracts).

CONCLUSIONS AND RELEVANCE

This study illustrates that convolutional neural networks can be used to automate the extraction of features of the built environment from satellite images for studying health indicators. Understanding the association between specific features of the built environment and obesity prevalence can lead to structural changes that could encourage physical activity and decreases in obesity prevalence.

Family lifestyle dynamics and childhood obesity: evidence from the millennium cohort study

Background

The prevalence of childhood obesity has been increasing but the causes are not fully understood. Recent public health interventions and guidance aiming to reduce childhood obesity have focused on the whole family, as opposed to just the child but there remains a lack of empirical evidence examining this relationship.

Methods

Using data from the longitudinal Millennium Cohort Study (MCS), we investigate the dynamic relationship between underlying family lifestyle and childhood obesity during early childhood. The MCS interviewed parents shortly after the birth of their child and follow up interviews were carried out when the child was 3, 5 and 7 years. We use a dynamic latent factor model, an approach that allows us to identify family lifestyle, its evolution over time (in this case between birth and 7 years) and its influence on childhood obesity and other observable outcomes.

Results

We find that family lifestyle is persistent, 87.43% of families which were above the 95th percentile on the lifestyle distribution, remained above the 95th percentile when the child was 7 years old. Family lifestyle has a significant influence on all outcomes in the study, including diet, exercise and parental weight status; family lifestyle accounts for 11.3% of the variation in child weight by age 7 years.

Conclusion

The analysis suggests that interventions should therefore be prolonged and persuasive and target the underlying lifestyle of a family as early as possible during childhood in order to have the greatest cumulative influence. Our results suggest that children from advantaged backgrounds are more likely to be exposed to healthier lifestyles and that this leads to inequalities in the prevalence of obesity. To reduce inequalities in childhood obesity, policy makers should target disadvantaged families and design interventions specifically for these families.

Food Shopping and Acquisition Behaviors in Relation to BMI among Residents of Low-Income Communities in South Carolina

Low-income areas in which residents have poor access to healthy foods have been referred to as "food deserts." It is thought that improving food access may help curb the obesity epidemic. Little is known about where residents of food deserts shop and if shopping habits are associated with body mass index (BMI). We evaluated the association of food shopping and acquisition (e.g., obtaining food from church, food pantries, etc.) with BMI among 459 residents of low-income communities from two South Carolina counties, 81% of whom lived in United States Department of Agriculture-designated food deserts. Participants were interviewed about food shopping and acquisition and perceptions of their food environment, and weight and height were measured. Distances to food retail outlets were determined. Multivariable linear regression analysis was employed. Our study sample comprising largely African-American women had an average BMI of 32.5 kg/m². The vast majority of study participants shopped at supermarkets (61%) or supercenters/warehouse clubs (27%). Shopping at a supercenter or warehouse club as one's primary store was significantly associated with a 2.6 kg/m² higher BMI compared to shopping at a supermarket, independent of demographics, socioeconomics, physical activity, and all other food shopping/acquisition behaviors. Persons who reported shopping at a small grocery store or a convenience or dollar store as their tertiary store had a 2.6 kg/m² lower BMI. Respondents who perceived lack of access to adequate food shopping in their neighborhoods as a problem had higher BMI. Living in a food desert census tract was not significantly associated with BMI. Other shopping attributes, including distance to utilized and nearest grocery stores, were not independently associated with BMI. These findings call into question the idea that poor spatial access to grocery stores is a key underlying factor affecting the obesity epidemic. Future research should consider assessing foods purchased and dietary intake within a comprehensive study of food shopping behaviors and health outcomes among persons living in food deserts.

The Relationship between Obesity and Urban Environment in Seoul

Obesity is a global pandemic that brings about a myriad of health consequences. In the past, policies for combating obesity mainly focused on improving individual health and behavior, but nowadays some policies have changed and now concentrate on improving the built environment believing this can improve health through positive changes to health-related behaviors. We examined whether both individual and environmental factors were associated with body mass index in Seoul, the capital city of South Korea. Data from the 2011 and 2013 Community Health Surveys were used (n = 20,147 men and 25,300 women). We staged multilevel logistic regression models to estimate the effect of individual and environmental factors on obesity. Among individual covariates, high-risk drinking, the time spent watching TV and surfing the Internet, high salt intake, stress, and the negative recognition of health were significantly associated with obesity. When controlling individual covariates, the number of sports facilities, number of fried chicken stores, and food insecurity level were statistically associated with probability of obesity. Therefore, this study emphasizes that it is important not only to improve the health behavior of the individual, but also to improve the urban environment in order to reduce the obesity rates of city dwellers.

Obesity and health-related decisions: An empirical model of the determinants of weight status across the transition from adolescence to young adulthood

We estimate a structural dynamic model of the determinants of obesity. In addition to including many of the well-recognized endogenous factors mentioned in the literature as obesity determinants, we also model the individual's residential location as a choice variable, which is the main contribution of this paper to the literature. This allows us to control for an individual's self-selection into communities that possess the types of amenities in the built environment, which in turn affect their obesity-related behaviors such as physical activity (PA) and fast food consumption. We specify reduced form equations for a set of endogenous demand decisions, together with an obesity structural equation. The whole system of equations is jointly estimated by a semi-parametric full information log-likelihood method that allows for a general pattern of correlation in the errors across equations. Our model predicts a reduction in adult obesity of 7 percentage points as a result of a continued high level PA from adolescence into adulthood; a reduction of 0.7 (3) percentage points in adult obesity as a result of one standard deviation reduction in weekly fast food consumption for women (men); and a reduction of 0.02 (0.05) in adult obesity as a result of one standard deviation change in several neighborhood amenities for women (men). Another key finding is that controlling for residential self-selection has substantive implications. To our knowledge, this has not been yet documented within a full information maximum likelihood framework.

Walking in Two French Neighborhoods: A Study of How Park Numbers and Locations Relate to Everyday Walking

Research indicates that people are drawn to green spaces with attractive amenities. This study extends that finding by comparing walking patterns in two neighborhoods with different numbers of parks; parks did not differ in rated attractiveness nor did neighborhoods differ substantially in rated walkability. Adults, aged 32-86 years (n = 90), drew their 3 most recent walking routes on maps of their neighborhood. Analyses showed that participants' round trips were longer by 265.5 meters (.16 mile) in the neighborhood with a single, large, centrally located park (p < .02). However, participants in the neighborhood with multiple, small, more distributed parks, visited more streets, p < .001, more streets with green spaces, p < .038, and used more varied routes, p < .012. Results suggest there are potential benefits to both layouts. Large centralized parks may invite longer walks; smaller, well-distributed parks may invite more varied routes suggestive of appropriation and motivation processes. Both layouts might be combined in a single neighborhood to attract more walkers.

The Urban Built Environment and Obesity in New York City: A Multilevel Analysis

Purpose.

To examine whether urban form is associated with body size within a densely-settled city.

Design.

Cross-sectional analysis using multilevel modeling to relate body mass index (BMI) to built environment resources.

Setting.

Census tracts (n = 1989) within the five boroughs of New York City.

Subjects.

Adult volunteers (n = 13,102) from the five boroughs of New York City recruited between January 2000 and December 2002.

Measures.

The dependent variable was objectively-measured BMI. Independent variables included land use mix; bus and subway stop density; population density; and intersection density. Covariates included age, gender, race, education, and census tract–level poverty and race/ethnicity.

Analysis.

Cross-sectional multilevel analyses.

Results.

Mixed land use (Beta = 2.55, p < .01), density of bus stops (Beta = −.01, p < .01) and subway stops (Beta = −.06, p < .01), and population density (Beta = −.25, p < .001), but not intersection density (Beta = −.002) were significantly inversely associated with BMI after adjustment for individual- and neighborhood-level sociodemographic characteristics. Comparing the 90th to the 10th percentile of each built environment variable, the predicted adjusted difference in BMI with increased mixed land use was −.41 units, with bus stop density was −.33 units, with subway stop density was −.34 units, and with population density was −.86 units.

Conclusion.

BMI is associated with built environment characteristics in New York City.

Obesity, diet quality, physical activity, and the built environment: the need for behavioral pathways

BACKGROUND

The built environment (BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet.

METHODS

The Seattle Obesity Study (SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index (HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity (PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months' exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors (HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally.

RESULTS

None of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict better diets and more physical activity. Better diets and more physical activity were associated with lower obesity prevalence at baseline and 12 mo, but did not predict weight change.

CONCLUSION

Any links between the BE and health outcomes critically depend on establishing appropriate behavioral pathways. In this study, home-centric BE measures, were not related to physical activity or to diet. Further studies will need to consider a broader range of BE attributes that may be related to diets and health.

What Constitutes an Obesogenic Environment in Rural Communities?

PURPOSE

To identify perceived indicators of the physical environment associated with obesity in rural communities.

DESIGN

Cross-sectional telephone survey.

SETTING

Thirteen communities in rural Missouri, Tennessee, and Arkansas, 2003.

SUBJECTS

A total of 2510 adults completed the survey and 2210 respondents were included in the analysis (74% female, 93% white, and 27% obese).

MEASURES

The 106-item survey measured perceptions of the neighborhood environment (recreational facilities, land use, transportation/safety, aesthetics, and food environment) and health-related behaviors. The primary outcome was obese (body mass index [BMI] > or = 30 kg/mn2) vs. normal weight (BMI = 18.5-24.9 kg/m2).

ANALYSIS

Logistic regression was used to control for age, gender and education.

RESULTS

Several indicators of the perceived neighborhood environment were associated with being obese (adjusted odds ratio [95% confidence interval]), including furthest distance to the nearest recreational facility (1.8 [1.3-2.4]), unpleasant community for physical activity (1.8 [1.3-2.6]), feeling unsafe from crime (2.1 [1.5-2.9]) or traffic (1.7 [1.2-2.3]), and few nonresidential destinations (1.4 [1.0-1.9]). Distance to recreational facilities and crime safety remained significant in the multivariate model, along with dietary-fat intake, sedentary behavior, and moderate/vigorous physical activity.

CONCLUSION

This study adds to a growing evidence base of environmental correlates of obesity and makes a unique contribution regarding rural communities. If causality is established, environmental interventions that target obesogenic neighborhood features may reduce the prevalence of obesity on a population level.

Social Disorder in Adults with Type 2 Diabetes: Building on Race, Place, and Poverty

The recent resurgence of social and civic disquiet in the USA has contributed to increasing recognition that social conditions are meaningfully connected to disease and death. As a "lifestyle disease," control of diabetes requires modifications to daily activities, including healthy dietary practices, regular physical activity, and adherence to treatment regimens. One's ability to develop the healthy practices necessary to prevent or control type 2 diabetes may be influenced by a context of social disorder, the disruptive social and economic conditions that influence daily activity and, consequently, health status. In this paper, we report on our narrative review of the literature that explores the associations between social disorder and diabetes-related health outcomes within vulnerable communities. We also propose a multilevel ecosocial model for conceptualizing social disorder, specifically focusing on its role in racial disparities and its pathways to mediating diabetes outcomes.

Design of a Health-Promoting Neighborhood Intervention

Design and implementation of health-promoting community interventions can advance public health and community well-being; however, realization of such programs is often challenging. Even more challenging is the implementation of ecologic interventions to revitalize built urban environments. A structured intervention entitled `Intersection Repair` was devised in Portland, Oregon, by a non-profit organization, to implement urban gathering places in the public right of way; specific steps included situation analysis, community outreach, asset mapping, design workshops, construction permitting, building workshops, and process evaluation. The community created human-scale urban landscapes with interactive art installations to encourage social interactions. Such aesthetic improvements, which included painted street murals, information kiosks, hanging gardens, water fountains, benches, and so on, were intended to strengthen social networks and social capital by providing places for residents to engage in conversation. Community engagement in neighborhood design benefits the public at multiple levels, by promoting a healthier lifestyle, over and above urban landscape improvements.

Built Environments and Active Living in Rural and Remote Areas: a Review of the Literature

Rural children and adults are more likely to have obesity than their urban counterparts even after adjustment for individual-level behaviors, suggesting that rural environments may promote obesity. The rural built environment may be an important area of research that can help us understand rural-urban disparities in obesity. The purpose of this review is to summarize the rural built environment and active living literature, and to address key issues, gaps, and observations in the field. A literature review was conducted in spring 2015 to identify research published from 2000 to 2015. Our review suggests that limited active living built environments in rural communities and unique rural barriers to physical activity may contribute to a higher prevalence of obesity compared to urban populations. More empirical research is needed to build the evidence-base for the association between rural built environments, active living, and obesity. School- and community-based policies that expand active living opportunities in rural areas should also be closely examined.

Do All Activities "Weigh" Equally? How Different Physical Activities Differ as Predictors of Weight

In Britain, it is recommended that, to stay healthy, adults should do 150 minutes of moderate-intensity physical activity every week. The recommendations provided by the U.K. government, however, remain silent in regard to the type of activity that should be done. Using the annual Health Survey for England we compare how different types of physical activities predict a person's weight. In particular, we consider clinically measured body mass index and waist circumference. We document mean slopes emanating from ordinary least squares regressions with these measures as the dependent variables. We show that individuals who walk at a brisk or fast pace are more likely to have a lower weight when compared to individuals doing other activities. Additionally, we highlight that the association between physical activity and weight is stronger for females and individuals over the age of 50. Our overall conclusions are robust to a number of specifications.

The Role of Urbanization in Childhood Obesity

Obesity is becoming the most frequently diagnosed chronic disease in many countries affecting all age groups and specifically the pediatric population. To date, most approaches have focused on changing the behavior of individuals with respect to diet and exercise. Almost all researchers agree that prevention could be the key strategy for controlling the current epidemic of obesity. Prevention may be achieved by changes in lifestyle through a variety of interventions targeting the urban environment, physical activity, time spent watching television and playing computer games and consumption of carbonated drinks. However, as yet, these strategies seem to have had little impact on the growing increase of the obesity epidemic. In this article, we aimed to discuss the effect of rapid urbanization on childhood obesity and to suggest solutions to this problem.

International study of perceived neighbourhood environmental attributes and Body Mass Index: IPEN Adult study in 12 countries

Background

Ecological models of health behaviour are an important conceptual framework to address the multiple correlates of obesity. Several single-country studies previously examined the relationship between the built environment and obesity in adults, but results are very diverse. An important reason for these mixed results is the limited variability in built environments in these single-country studies. Therefore, the aim of this study was to examine associations between perceived neighbourhood built environmental attributes and BMI/weight status in a multi-country study including 12 environmentally and culturally diverse countries.

Methods

A multi-site cross-sectional study was conducted in 17 cities (study sites) across 12 countries (Australia, Belgium, Brazil, China, Colombia, Czech Republic, Denmark, Mexico, New Zealand, Spain, the UK and USA). Participants (n = 14222, 18–66 years) self-reported perceived neighbourhood environmental attributes. Height and weight were self-reported in eight countries, and measured in person in four countries.

Results

Three environmental attributes were associated with BMI or weight status in pooled data from 12 countries. Safety from traffic was the most robust correlate, suggesting that creating safe routes for walking/cycling by reducing the speed and volume of traffic might have a positive impact upon weight status/BMI across various geographical locations. Close proximity to several local destinations was associated with BMI across all countries, suggesting compact neighbourhoods with more places to walk related to lower BMI. Safety from crime showed a curvilinear relationship with BMI, with especially poor crime safety being related to higher BMI.

Conclusions

Environmental interventions involving these three attributes appear to have international relevance and focusing on these might have implications for tackling overweight/obesity.

Associations between neighborhood effects and physical activity, obesity, and diabetes: The Jamaica Health and Lifestyle Survey 2008

OBJECTIVE

To examine the impact of neighborhood disorder, perceived neighborhood safety, and availability of recreational facilities on prevalence of physical activity (PA), obesity, and diabetes mellitus (DM).

STUDY DESIGN AND SETTING

Multilevel analyses were conducted among 2,848 respondents from the 2007-08 Jamaica Health and Lifestyle Survey. Neighborhood effects were based on aggregated interviewer responses to systematic social observation questions. Mixed-effect logistic regression models were created to assess the relationship between neighborhood indicators and DM and the modifiable risk factors PA and overweight/obesity.

RESULTS

There was significant clustering in PA levels of 20 minutes at least once per week (intraclass correlation coefficient [ICC] = 10.7%), low/no PA (ICC = 7.22%), diabetes (ICC = 5.44%), and obesity (ICC = 3.33%) across neighborhoods. Greater levels of neighborhood disorder, home disorder, and counterintuitively recreational space availability were associated with higher levels of low/no PA among women. There was significant interaction by sex between neighborhood infrastructure and overweight/obesity with a significant association in men (odds ratio [OR] = 1.16; 95% confidence interval [CI] = 1.05, 1.28) but not women (OR = 1.01; 95% CI = 0.95, 1.07).

CONCLUSION

Differences in PA and obesity-related outcomes among Jamaicans may be partially explained by characteristics of the neighborhood environment and differ by sex. Future studies must be conducted to determine the mechanistic pathways through which the neighborhood environment may impact such outcomes to better inform prevention efforts.