Experiences of connectivity and severance in the wake of a new motorway: Implications for health and well-being

Changing mobility practices. Can meta-ethnography inform transferable and policy-relevant theory?

Social practice theories have attracted attention for their potential insights into how to change transport systems towards "healthier" states. However, most evidence is from small-scale qualitative case studies. We explored whether a synthesis of qualitative evidence on mobility practices in one country, informed by meta-ethnography and a Bourdieusian approach to practice, could produce theory that is of sufficient abstraction to be transferable, yet also capable of informing intervention planning. The synthesis identified three third order constructs: mobility practices result from habitus plus capital in fields; specific configurations of local mobility practices are shaped, but not determined, by material infrastructures and social structures; and changes in practice happen across a number of scales and temporalities. This body of evidence as a whole was then interpreted as an integrative "storyline": Mobility systems are complex, in that outcomes from interventions are neither unilinear nor necessarily predictable from aggregations of individual practice changes. Infrastructure changes may be a necessary, but not sufficient, condition for change. Moving systems towards "healthier" states requires changing habitus such that "healthier" practices align with fields, and that interventions take sufficient account of the power relations that materially and symbolically constrain or enable attachments to and changes in mobility practices. Meta-ethnography is a useful approach for integrating qualitative evidence for informing policy.

Barrier Effect in a Medium-Sized Brazilian City: An Exploratory Analysis Using Decision Trees and Random Forests

This study aims to examine if an urban road with intense motorized traffic in a medium-sized Brazilian city constitutes a barrier for walking trips. A questionnaire was conducted with 103 individuals in an area up to 800 m from the road selected for the study to obtain information about personal characteristics (age, income, etc.), social interactions in the neighborhood, and travel and mobility characteristics. We used the dataset to explore the potential of Decision Tree and Random Forest classification models to predict the users’ perception of the barrier effect, which was characterized by the dependent variables speed and volume (of motorized traffic). For 36.9% and 47.6% of respondents, traffic speed and traffic volume, respectively, represent a barrier to walking. The results also show that the following variables considerably affect the perception of the barrier effect of the respondents: distance from their residence to the studied road, time living at the address and in the study area, social connections in the neighborhood, and the street reported as the busiest one in the neighborhood. Identifying the variables with the largest influence on the perception of the barrier effect may be very useful for planning and policy initiatives.

Sustainability and Resilience of Indigenous Siberian Communities under the Impact of Transportation Infrastructure Transformation

Transport provision in remote territories is one of the most important factors in maintaining the sustainability of socio-economic and ecological systems. Indigenous peoples of Siberia have always been mobile using diverse traditional ways across the taiga. During the Soviet period, the transition to settled life, along with technological development and the emergence of new modes of transport, such as off-road cars, snowmobiles, and motor boats, significantly affected the level of population mobility, including remote areas where people are engaged in traditional nature management. With the collapse of the Soviet Union, there were significant changes and reductions in the subsidies of transportation systems for remote terrains that made them isolated. Transport connectivity has been realized through rare plane flights (once a month or less) or by cars on dirt roads (actually off-road) that take several days of travel. Siberian territories rich with natural resources, low population density, and weak infrastructure might be attractive for mining companies. Being difficult to access not only for the local population, but also for industrial companies, the territories imply the allocation of a significant share of road construction and transport costs in the cost items of miners and processors. The problems of sustainability and resilience of the indigenous peoples of Siberia require special attention when restructuring transport communications, but they have practically not been studied before. Methods of in-depth and group interviews with local residents were used. Based on comparative geographic and statistical analysis and generalization of data, network and problem approaches applied for various sources and field materials (including in-depth and group interviews), the factors of sustainability and resilience, which the indigenous communities of three remote Siberian territories pin their hopes on in the context of the transformation of transportation infrastructure, have been identified. If geographic remoteness remains an unchanged fact, the expansion of desired transport accessibility (mainly due to investments by industrial companies) is associated by locals with the possibility of additional income related to provision of services, the emergence of new types of employment of the population that have not been observed before, and the implementation of new transport modes to support traditional activities.

Living liveable? RESIDE's evaluation of the "Liveable Neighborhoods" planning policy on the health supportive behaviors and wellbeing of residents in Perth, Western Australia

BACKGROUND

The RESIDential Environments (RESIDE) project is a unique longitudinal natural experiment designed to evaluate the health impacts of the "Liveable Neighbourhoods" planning policy, which was introduced by the Western Australian government to create more walkable suburbs.

OBJECTIVES

To summarize the RESIDE evidence of the impact of the planning policy on a range of health-supportive behaviours and wellbeing outcomes and to assess the consistency and direction of the estimates of associations.

METHODS

An audit of 26 RESIDE research papers (from 2003 to 2012) identified the number of positive associations (statistically significant and consistent with policy expectations), negative associations (statistically significant and inconsistent with policy expectations), and null findings from multiple-exposure models between objective and perceived measures of 20 policy design requirements and 13 health-supportive behaviors and wellbeing outcomes.

RESULTS

In total 332 eligible estimates of associations (n = 257 objective measures and n = 75 perceived measures) were identified. Positively significant findings were detected for: 57% of walking estimates with objectively measured policy design features (negative = 3%; null = 40%) (n = 115) and 54% perceived measures (negative = 0%; null = 33%) (n = 27); 42% of sense of community estimates with objectively measured of policy design features (negative = 8%; null = 50%) (n = 12) and 61% perceived measures (negative = 8%; null = 31%) (n = 13); 39% of safety or crime-related estimates with objectively measured of policy design features (negative = 22%; null = 39%) (n = 28) and 100% perceived measures (n = 7). All (n = 4) estimates for mental health outcomes with objectively measured policy-related design features were positively significant.

CONCLUSIONS

The synthesis of findings suggests that new suburban communities built in accordance with the "Liveable Neighbourhoods" policy have the potential to encourage health supportive behaviors and wellbeing outcomes including transport and recreation walking, and to create neighborhoods with a stronger sense of community where residents may feel safer.

[Influence of daily and weekly activities in the physical activity levels of school children: a cross-sectional study]

Introduction

Introduction: the distinct activities related to an active lifestyle that schoolchildren perform during the day and week interact to influence the children's total level of physical activity. Objective: to identify the influence that the activities developed by school children during the different times of the day and the week have on the levels of physical activity. Method: a cross-sectional study conducted in 3,598 public school students aged between 10 and 14 years. The demographic characteristics were evaluated, as well as the levels of physical activity through the Physical Activity Questionnaire for Schoolchildren (PAQ-C). A multivariate logistic regression analysis was performed as statistical analysis using the SPSS v.24 software program. The raw odds ratio (OR) was estimated. Results: in all, 64.3% of the students failed to comply with physical activity recommendations. Students who are more inactive include those who do not perform enough physical activity during the weekend [OR: 3.7 (95% CI: 2.9-4.5)], those who are not physically active after school [OR: 3.1 (95% CI: 2.3-4.2)], those who do not practice activities such as jumping in their free time [OR: 3.2 (95% CI: 1.9-5.2)], and those who do not ride a bicycle [OR: 3.7 (95% CI: 2.7-5.1)]. Conclusions: the levels of physical activity in students depend on the integration of different contexts and periods during the day and the week. The influence of weekend activities and physical education shows the role that the family and school play in the acquisition and practice of an active lifestyle.

Gender and active travel: a qualitative data synthesis informed by machine learning

BACKGROUND

Innovative approaches are required to move beyond individual approaches to behaviour change and develop more appropriate insights for the complex challenge of increasing population levels of activity. Recent research has drawn on social practice theory to describe the recursive and relational character of active living but to date most evidence is limited to small-scale qualitative research studies. To 'upscale' insights from individual contexts, we pooled data from five qualitative studies and used machine learning software to explore gendered patterns in the context of active travel.

METHODS

We drew on 280 transcripts from five research projects conducted in the UK, including studies of a range of populations, travel modes and settings, to conduct unsupervised 'topic modelling analysis'. Text analytics software, Leximancer, was used in the first phase of the analysis to produce inter-topic distance maps to illustrate inter-related 'concepts'. The outputs from this first phase guided a second researcher-led interpretive analysis of text excerpts to infer meaning from the computer-generated outputs.

RESULTS

Guided by social practice theory, we identified 'interrelated' and 'relating' practices across the pooled datasets. For this study we particularly focused on respondents' commutes, travelling to and from work, and on differentiated experiences by gender. Women largely described their commute as multifunctional journeys that included the school run or shopping, whereas men described relatively linear journeys from A to B but highlighted 'relating' practices resulting from or due to their choice of commute mode or journey such as showering or relaxing. Secondly, we identify a difference in discourses about practices across the included datasets. Women spoke more about 'subjective', internal feelings of safety ('I feel unsafe'), whereas men spoke more about external conditions ('it is a dangerous road').

CONCLUSION

This rare application of machine learning to qualitative social science research has helped to identify potentially important differences in co-occurrence of practices and discourses about practice between men's and women's accounts of travel across diverse contexts. These findings can inform future research and policy decisions for promoting travel-related social practices associated with increased physical activity that are appropriate across genders.

Semiautomated text analytics for qualitative data synthesis

Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. This case study aimed to (a) provide a step‐by‐step guide to using one software application, Leximancer, and (b) interrogate opportunities and limitations of the software for qualitative data synthesis. We applied Leximancer v4.5 to a pool of five qualitative, UK‐based studies on transportation such as walking, cycling, and driving, and displayed the findings of the automated content analysis as intertopic distance maps. Leximancer enabled us to “zoom out” to familiarize ourselves with, and gain a broad perspective of, the pooled data. It indicated which studies clustered around dominant topics such as “people.” The software also enabled us to “zoom in” to narrow the perspective to specific subgroups and lines of enquiry. For example, “people” featured in men's and women's narratives but were talked about differently, with men mentioning “kids” and “old,” whereas women mentioned “things” and “stuff.” The approach provided us with a fresh lens for the initial inductive step in the analysis process and could guide further exploration. The limitations of using Leximancer were the substantial data preparation time involved and the contextual knowledge required from the researcher to turn lines of inquiry into meaningful insights. In summary, Leximancer is a useful tool for contributing to qualitative data synthesis, facilitating comprehensive and transparent data coding but can only inform, not replace, researcher‐led interpretive work.

INTERACT: A comprehensive approach to assess urban form interventions through natural experiments

BACKGROUND

Urban form interventions can result in positive and negative impacts on physical activity, social participation, and well-being, and inequities in these outcomes. Natural experiment studies can advance our understanding of causal effects and processes related to urban form interventions. The INTErventions, Research, and Action in Cities Team (INTERACT) is a pan-Canadian collaboration of interdisciplinary scientists, urban planners, and public health decision makers advancing research on the design of healthy and sustainable cities for all. Our objectives are to use natural experiment studies to deliver timely evidence about how urban form interventions influence health, and to develop methods and tools to facilitate such studies going forward.

METHODS

INTERACT will evaluate natural experiments in four Canadian cities: the Arbutus Greenway in Vancouver, British Columbia; the All Ages and Abilities Cycling Network in Victoria, BC; a new Bus Rapid Transit system in Saskatoon, Saskatchewan; and components of the Sustainable Development Plan 2016-2020 in Montreal, Quebec, a plan that includes urban form changes initiated by the city and approximately 230 partnering organizations. We will recruit a cohort of between 300 and 3000 adult participants, age 18 or older, in each city and collect data at three time points. Participants will complete health and activity space surveys and provide sensor-based location and physical activity data. We will conduct qualitative interviews with a subsample of participants in each city. Our analysis methods will combine machine learning methods for detecting transportation mode use and physical activity, use temporal Geographic Information Systems to quantify changes to urban intervention exposure, and apply analytic methods for natural experiment studies including interrupted time series analysis.

DISCUSSION

INTERACT aims to advance the evidence base on population health intervention research and address challenges related to big data, knowledge mobilization and engagement, ethics, and causality. We will collect ~ 100 TB of sensor data from participants over 5 years. We will address these challenges using interdisciplinary partnerships, training of highly qualified personnel, and modern methodologies for using sensor-based data.