The majority of people now live in an urban (or suburban) environment. The built (material) environment, its vehicular and pedestrian infrastructure, buildings, and public realm places, are the places used for working, living, and recreating. The environment currently favors and facilitates motorized vehicles generally, and private automobiles especially. The prioritization given to vehicles reduces opportunities for other, more active modes of travel such as walking and bicycling. Though the built environment cannot be said to directly affect human obesity, the built environment clearly has a relationship to obesity as a consequence of physical activity. Most concerning is that rates of obesity have risen as cars have become increasingly privileged, leading to places that favor driving over walking or bicycling. This review examines current empirical literature on the environment and obesity at 3 key urban scales: macro, meso, and micro. Other key issues examined include longitudinal studies and self-selection bias. Evidence for a relationship between urban and suburban environments and obesity is found in the literature, but the lack of longitudinal research and research controlling for self-selection bias remains underrepresented.
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http://dx.doi.org/10.1093/nutrit/nuw037 | DOI Listing |
Front Public Health
January 2025
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China.
Introduction: The healthy aging of older adults in dual-older adult communities is influenced by multiple factors, and understanding its underlying mechanisms can promote healthy aging among the older adults in a wide range of developing countries. This comprehensive study delves into the intricate interplay between multifaceted built environmental factors, and their direct and indirect effects on the successful AIP residing in double-aging neighborhoods.
Methods: Applying a series of HLM, the research meticulously explores the intricate links between SAIP and multi-scale aging spaces, including home space, community social participation, and built environments.
Water Res
January 2025
Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK; SWING - Department of Built Environment, Oslo Metropolitan University, St Olavs plass 0130, Oslo, Norway. Electronic address:
Climate resilience in water resource recovery facilities (WRRFs) necessitates improved adaptation to shock-loading conditions and mitigating greenhouse gas emission. Data-driven learning methods are widely utilised in soft-sensors for decision support and process optimization due to their simplicity and high predictive accuracy. However, unlike for mechanistic models, transferring machine-learning-based insights across systems is largely infeasible, which limits communication and knowledge sharing.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Geography, School of GeoSciences, University of Edinburgh, Edinburgh, UK.
Research has proved a close relationship between environments and physiological as well as psychological responses. However, existing research based on neuroscience experiments demonstrated a clear dichotomy between natural and built environments in the selection of exposure settings. There is very limited research analyzing and comparing the effects of different urban environments on individual psychological health.
View Article and Find Full Text PDFSci Data
January 2025
Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham, B4 7XG, UK.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland.
View Article and Find Full Text PDFInfect Control Hosp Epidemiol
January 2025
Department of Biostatistics and Data Science, Wake Forest University, School of Medicine, Medical Center Blvd, Winston-Salem, NC27157, USA.
Objective: Environmental features of a patient's room depend on the patient's level of acuity and their clinical manifestations upon admission and during their hospital stay. In this study, we wish to apply statistical methodology to explore the association between room features and hospital onset infections caused by (HO-CDI) while accounting for room assignment.
Method: We conducted a nested case-control study using retrospective electronic health record (EHR) data of patients hospitalized at the Ohio State University Wexner Medical Center (OSUWMC) between January 2019 and April 2021.
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