Lexical neutrality in environmental health research: Reflections on the term walkability

Association of Public Sports Space Perception with Health-Related Quality of Life in Middle-Aged and Older Adults-Evidence from a Survey in Shandong, China

Creating a healthy living environment for middle-aged and older adults is a key strategy for countries to address the aging challenge, but the effects of such an environment on the health-related quality of life (HRQoL) of middle-aged and older adults remain underexplored. This study aimed to examine the link between public sports facilities and the HRQoL of middle-aged and older adult residents in communities. A total of 1169 respondents (average age: 66.84; male: 46.19%) were selected from the Shandong, China. This study measured respondents 'physical activity (PA) using the International Physical Activity Questionnaire, the HRQoL of respondents using the 36-item Short Form Health Survey, and the Public Sports Space Perception Scale for respondents' public sports space perception. Correlation analysis and logistic regression analysis were employed to test the relationship among public sports space perception, physical activity (PA), and HRQoL. The mediating role of PA was conducted using the PROCESS macro for SPSS. The results revealed that public sports space perception only influenced the HRQoL of middle-aged and older adults through light-intensity PA (PCS: B  =  0.09, 95% CI 0.01, 0.03; MCS: B  =  0.02, 95% CI 0.01, 0.05) among light-intensity PA, moderate-intensity PA, vigorous-intensity PA., and this mediation model varied across different age groups of middle-aged and older adults. Moreover, digital inclusion only moderated the psychological aspect of HRQoL of middle-aged and older adults (p < 0.05). This study provided empirical evidence for enhancing the HRQoL of middle-aged and older adults and offered useful insights for the planning and design of public sports facilities and the formulation of health management policies for middle-aged and older adults.

Individual physical activity, neighbourhood active living environment and mental illness hospitalisation among adults with cardiometabolic disease: a Canadian population-based cohort analysis

OBJECTIVES

This population-based observational study explores the associations between individual-level and neighbourhood-level indices of active living with inpatient mental healthcare use among adults with an underlying chronic cardiometabolic condition.

DESIGN AND SETTING

Data from the 2013-2014 Canadian Community Health Survey were linked longitudinally to hospital records from the 2013/2014‒2017/2018 Discharge Abstract Database and to a geocoded measure of active living environments (ALE). Relationships between individuals' leisure-time physical activity and neighbourhood ALE with risk of hospital admission for mental health disorders were assessed using multivariable Cox regressions.

PARTICIPANTS

A national cohort was identified from the survey data of 24 960 respondents aged 35 years and above reporting having been diagnosed with diabetes, hypertension and/or heart disease.

OUTCOME MEASURE

Potentially avoidable hospitalisation for a mood, anxiety or substance use disorder over a 5-year period.

RESULTS

More than half (52%) of adults aged 35 years and above with a cardiometabolic disease were physically inactive in their daily lives, and one-third (34%) resided in the least activity-friendly neighbourhoods. The rate of being hospitalised at least once for a comorbid mental disorder averaged 8.1 (95% CI: 7.0 to 9.3) per 1000 person-years of exposure. Individuals who were at least moderately active were half as likely to be hospitalised for a comorbid mental health problem compared with those who were inactive (HR: 0.50 (95% CI: 0.38 to 0.65)). No statistically discernible associations between neighbourhood ALE and hospitalisation risks were found after controlling for individuals' behaviours and characteristics, including in separate models stratified by age group and by sex.

CONCLUSIONS

The evidence base to support prioritisation of interventions focusing on the built environment favouring mental health-promoting physical activity among higher-risk adults at the population level, independently of individual-level behaviours and characteristics, remains limited.

ALF-Score++, a novel approach to transfer knowledge and predict network-based walkability scores across cities

Walkability is an important measure with strong ties to our health. However, there are existing gaps in the literature. Our previous work proposed new approaches to address existing limitations. This paper explores new ways of applying transferability using transfer-learning. Road networks, POIs, and road-related characteristics grow/change over time. Moreover, calculating walkability for all locations in all cities is very time-consuming. Transferability enables reuse of already-learned knowledge for continued learning, reduce training time, resource consumption, training labels and improve prediction accuracy. We propose ALF-Score++, that reuses trained models to generate transferable models capable of predicting walkability score for cities not seen in the process. We trained transfer-learned models for St. John’s NL and Montréal QC and used them to predict walkability scores for Kingston ON and Vancouver BC. MAE error of 13.87 units (ranging 0–100) was achieved for transfer-learning using MLP and 4.56 units for direct-training (random forest) on personalized clusters.

ALF–Score—A novel approach to build a predictive network–based walkability scoring system

Walkability is a term that describes various aspects of the built and social environment and has been associated with physical activity and public health. Walkability is subjective and although multiple definitions of walkability exist, there is no single agreed upon definition. Road networks are integral parts of mobility and should be an important part of walkability. However, using the road structure as nodes is not widely discussed in existing methods. Most walkability measures only provide area–based scores with low spatial resolution, have a one–size–fits–all approach, and do not consider individuals opinion. Active Living Feature Score (ALF–Score) is a network–based walkability measure that incorporates road network structures as a core component. It also utilizes user opinion to build a high–confidence ground–truth that is used in our machine learning pipeline to generate models capable of estimating walkability. We found combination of network features with road embedding and points of interest features creates a complimentary feature set enabling us to train our models with an accuracy of over 87% while maintaining a conversion consistency of over 98%. Our proposed approach outperforms existing measures by introducing a novel method to estimate walkability scores that are representative of users opinion with a high spatial resolution, for any point on the road.

Rethinking walkability and developing a conceptual definition of active living environments to guide research and practice

BACKGROUND

Walkability is a popular term used to describe aspects of the built and social environment that have important population-level impacts on physical activity, energy balance, and health. Although the term is widely used by researchers, practitioners, and the general public, and multiple operational definitions and walkability measurement tools exist, there are is no agreed-upon conceptual definition of walkability.

METHOD

To address this gap, researchers from Memorial University of Newfoundland hosted "The Future of Walkability Measures Workshop" in association with researchers from the Canadian Urban Environmental Health Research Consortium (CANUE) in November 2017. During the workshop, trainees, researchers, and practitioners worked together in small groups to iteratively develop and reach consensus about a conceptual definition and name for walkability. The objective of this paper was to discuss and propose a conceptual definition of walkability and related concepts.

RESULTS

In discussions during the workshop, it became clear that the term walkability leads to a narrow conception of the environmental features associated with health as it inherently focuses on walking. As a result, we suggest that the term Active Living Environments, as has been previously proposed in the literature, are more appropriate. We define Active Living Environments (ALEs) as the emergent natural, built, and social properties of neighbourhoods that promote physical activity and health and allow for equitable access to health-enhancing resources.

CONCLUSIONS

We believe that this broader conceptualization allows for a more comprehensive understanding of how built, natural, and social environments can contribute to improved health for all members of the population.

Walkability measures to predict the likelihood of walking in a place: A classification and regression tree analysis

Walkability is a popular and ubiquitous term at the intersection of urban planning and public health. As the number of potential walkability measures grows in the literature, there is a need to compare their relative importance for specific research objectives. This study demonstrates a classification and regression tree (CART) model to compare five familiar measures of walkability from the literature for their relative ability to predict whether or not walking occurs in a dataset of objectively measured locations. When analyzed together, the measures had moderate-to-high accuracy (87.8% agreement: 65.6% of true walking GPS-measured points classified as walking and 93.4% of non-walking points as non-walking). On its own, the most well-known composite measure, Walk Score, performed only slightly better than measures of the built environment composed of a single variable (transit ridership, employment density, and residential density).Thus there may be contexts where transparent and longitudinally available measures of urban form are worth a marginal tradeoff in prediction accuracy. This comparison of walkability measures using CART highlights the importance for public health and urban design researchers to think carefully about how and why particular walkability measures are used.

Unpacking walkability indices and their inherent assumptions

Walkability indices are used to characterize the relationship between health and place. Indices make assumptions that affect analysis of the built environment and resulting walkability scores. This study compares three walkability indices created by health researchers focusing on the methods, variables, and walkability scores resulting from differences in definitions and methods. This paper deconstructs the walkability algorithms utilized by each index and rebuilds them in Vancouver, Canada. We find that neighbourhoods in the northern core closer to the downtown area have similar walkability scores across all three indices, while the outer peripheral neighbourhoods with moderate to low walkability have more variation in walkability scores across indices. Most walkability variables - residential density, street connectivity, and land-use - lack a rationale for inclusion, often assumed by researchers. Walkability indices used in health research prove to be incongruent with each other and misrepresentative of actual human behavior. We explore the impact of variable selection and methodologies on indices in the interest of more rigorous health research.