The effects of the built environment on the general health, physical activity and obesity of adults in Queensland, Australia

Developing a transit desert interactive dashboard: Supervised modeling for forecasting transit deserts

Transit deserts refer to regions with a gap in transit services, with the demand for transit exceeding the supply. This study goes beyond merely identifying transit deserts to suggest actionable solutions. Using a multi-class supervised machine learning framework, we analyzed factors leading to transit deserts, distinguishing demand by gender. Our focus was on peak-time periods. After assessing the Support Vector Machine, Decision Tree, Random Forest, and K-nearest Neighbor, we settled on the Random Forest method, supported by Diverse Counterfactual Explanation and SHapley Additive Explanation in our analysis. The ranking of feature importance in the trained Random Forest model revealed that factors such as density, design, distance to transit, diversity in the built environment, and sociodemographic characteristics significantly contribute to the classification of transit deserts. Diverse Counterfactual Explanation suggested that a reduction in population density and an increase in the proportion of green open spaces would likely facilitate the transformation of transit deserts into transit oases. SHapley Additive Explanation highlighted the differential impact of various features on each identified transit desert. Our analysis results indicate that identifying transit deserts can vary depending on whether the data is aggregated or separated by demographics. We found areas that have unique transit needs based on gender. The disparity in transit services was particularly pronounced for women. Our model pinpointed the core elements that define a transit desert. Broadly, to address transit deserts, strategies should prioritize the needs of disadvantaged groups and enhance the design and accessibility of transit in the built environment. Our research extends existing analyses of transit deserts by leveraging machine learning to develop a predictive model. We developed a machine learning-powered interactive dashboard. Integrating participatory planning approaches with the development of an interactive interface could enhance ongoing community engagement. Planning practices can evolve with AI in the loop.

Impacts of active mobility on individual health mediated by physical activities

Active mobility, encompassing walking and cycling for transportation, is a potential solution to health issues arising from inadequate physical activity in modern society. However, the extent of active mobility's impact on individual physical activity levels, and its association with health as mediated by physical activities, is not fully quantified. This study aims to clarify the direct relationship between active mobility usage and individual health, as well as the indirect relationship mediated by physical activity, with a focus on varying levels of physical activity intensity. Utilizing data from the 2017 U.S. National Household Travel Survey (NHTS), we employed Poisson regression to predict active mobility usage based on socio-demographic and household socio-economic characteristics. A Structural Equation Model (SEM) was then used to investigate the direct and indirect effects of active mobility on individual health, mediated by physical activity. We further segmented individuals according to their intensity of physical activity to examine how such effect differs between different levels of physical activity. The study demonstrates that active mobility usage positively correlates with both the amount and intensity of physical activity. The effect of active mobility on individual health includes a direct positive effect (29% for intensity, 67.7% for amount) and an indirect effect mediated by physical activity (71% for intensity, 32.3% for amount). Notably, the mediation effect of active mobility on health is more substantial in the context of vigorous physical activities compared to light or moderate activities. Our findings reveal a significant positive influence of active mobility on individual health, encompassing both direct and indirect effects mediated by physical activities. These results quantitatively underscore the health benefits of active mobility and suggest the importance of promoting active mobility as a strategy to improve public health.

Sleep duration, sleep quality and the risk of being obese: Evidence from the Australian panel survey

BACKGROUND

Sleep difficulty is an unmet public health concern affecting a vast proportion of the world's population. Poor sleep duration (short or long sleep length) and quality affect more than half of older people. Sleep difficulty is associated with negative health outcomes such as obesity and reduced longevity. We aimed to assess whether poor sleep duration and quality are significant risk factors for obesity in adults aged 15 and over in Australia by examining a nationally representative panel data.

METHODS

We used three waves (waves 13, 17, and 21) of the nationally representative Household, Income and Labour Dynamics in Australia (HILDA) survey data. The study applied generalized estimating equations (GEE) logistic regression model to assess the relationship between sleep duration and quality with obesity.

RESULTS

The study found that the odds of being obese was significantly higher amongst the study participants with poor sleep duration (adjusted odds ratio [aOR]: 1.24, 95% confidence interval [CI]: 1.16-1.32) and poor sleep quality (aOR: 1.29, 95% CI: 1.02-1.38) compared with their counterparts who had good sleep duration and quality, respectively.

CONCLUSION

Having short or long sleep at night and poor sleep quality are associated with an increased risk of obesity. Obesity poses a significant threat to the health of Australian adults. Enacting policies that raise public awareness of the significance of good sleep hygiene and encouraging healthy sleeping habits should be considered to address the alarming rise in the obesity rate.

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments

BACKGROUND

The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic.

METHODS

We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines.

RESULTS

The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident.

CONCLUSION

The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.

How the Built Environment Promotes Residents' Physical Activity: The Importance of a Holistic People-Centered Perspective

Promoting adequate physical activity (PA) such as walking and cycling is essential to cope with the global health challenge of non-communicable diseases (NCDs). Much research has been conducted to analyze how the built environment can promote PA, but the results are not consistent. Some scholars found that certain built environments such as green spaces generated positive impacts on PA, while some other studies showed no correlations. We suspected that the built environment should be measured in a deeply holistic nuanced way in order to properly reflect its impact on PA. Therefore, our research adopted an integral urban-analysis comparing three typical neighborhoods in Beijing, China. Our data show that the highest PA occurs in the neighborhood with the lowest density, amount of green space and street connectivity, apparently compensated by its low-rise housing type and high appreciation of the quality of sidewalks and street safety. This indicates that dimensions impacting PA have to be considered in context, and the peoples’ perception of the built environment matters.

A Geographical Analysis of Socioeconomic and Environmental Drivers of Physical Inactivity in Post Pandemic Cities: The Case Study of Chicago, IL, USA

The pandemic’s lockdown has made physical inactivity unavoidable, forcing many people to work from home and increasing the sedentary nature of their lifestyle. The link between spatial and socio-environmental dynamics and people’s levels of physical activity is critical for promoting healthy lifestyles and improving population health. Most studies on physical activity or sedentary behaviors have focused on the built environment, with less attention to social and natural environments. We illustrate the spatial distribution of physical inactivity using the space scan statistic to supplement choropleth maps of physical inactivity prevalence in Chicago, IL, USA. In addition, we employ geographically weighted regression (GWR) to address spatial non-stationarity of physical inactivity prevalence in Chicago per census tract. Lastly, we compare GWR to the traditional ordinary least squares (OLS) model to assess the effect of spatial dependency in the data. The findings indicate that, while access to green space, bike lanes, and living in a diverse environment, as well as poverty, unsafety, and disability, are associated with a lack of interest in physical activities, limited language proficiency is not a predictor of an inactive lifestyle. Our findings suggest that physical activity is related to socioeconomic and environmental factors, which may help guide future physical activity behavior research and intervention decisions, particularly in identifying vulnerable areas and people.