Is practice aligned with the principles? Implementing New Urbanism in Perth, Western Australia

New Urbanism is a recent American reform approach to urban development, which attempts to reduce car dependence through traditional design qualities such as connected streets with paths, higher density and mix with local centres. The Western Australian State Government has developed ‘Liveable Neighbourhoods’, which is a context-specific design code based on new Urbanist principles. This design code has been applied in the development of several dozen new neighbourhoods in Perth over the last decade. This paper shows that these developments do create more local walking but are no different to conventional suburban development in their regional car dependence. The causes of this are pursued in terms of a gap between principles and practice

Special Libraries, January-February 1933

Volume 24, Issue 1https://scholarworks.sjsu.edu/sla_sl_1933/1000/thumbnail.jp

Kids in Communities Study (KiCS) study protocol: a cross-sectional mixed-methods approach to measuring community-level factors influencing early child development in Australia

Introduction: Healthy childhood development in the early years is critical for later adult health and well-being. Early childhood development (ECD) research has focused primarily on individual, family and school factors, but largely ignored community factors. The Kids in Communities Study (KiCS) will test and investigate community-level influences on child development across Australia. Methods and analysis: Cross-sectional mixed-methods study exploring community-level effects in 25 Australian local communities; selection based on community socioeconomic status (SES) and ECD using the Australian Early Development Census (AEDC), a population measure of child development, to create a local community 'diagonality type', that is, those performing better or worse (off-diagonal), or as expected (on-diagonal) on the AEDC relative to their SES. Data collection includes stakeholder interviews, parent and service provider focus groups, and surveys with general community residents and service providers, mapping of neighbourhood design and local amenities and services, analysis of policy documents, and the use of existing sociodemographic and early childhood education and care data. Quantitative data will be used to test associations between local community diagonality type, and ECD based on AEDC scores. Qualitative data will provide complementary and deeper exploration of these same associations. Ethics and dissemination: The Royal Children's Hospital Human Research Ethics Committee approved the study protocol (#30016). Further ethics approvals were obtained from State Education and Health departments and Catholic archdioceses where required. ECD community-level indicators will eventually be derived and made publically available. Findings will be published in peer-reviewed journals, community reports, websites and policy briefs to disseminate results to researchers, and key stakeholders including policymakers, practitioners and (most importantly) the communities involved.Sharon Goldfeld, Karen Villanueva, Robert Tanton, Ilan Katz, Sally Brinkman, Geoffrey Woolcock, Billie Giles-Cort

The effect of moving to East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village, on mode of travel (ENABLE London study, a natural experiment)

Background Interventions to encourage active modes of travel (walking, cycling) may improve physical activity levels, but longitudinal evidence is limited and major change in the built environment / travel infrastructure may be needed. East Village (the former London 2012 Olympic Games Athletes Village) has been repurposed on active design principles with improved walkability, open space and public transport and restrictions on residential car parking. We examined the effect of moving to East Village on adult travel patterns. Methods One thousand two hundred seventy-eight adults (16+ years) seeking to move into social, intermediate, and market-rent East Village accommodation were recruited in 2013–2015, and followed up after 2 years. Individual objective measures of physical activity using accelerometry (ActiGraph GT3X+) and geographic location using GPS travel recorders (QStarz) were time-matched and a validated algorithm assigned four travel modes (walking, cycling, motorised vehicle, train). We examined change in time spent in different travel modes, using multilevel linear regresssion models adjusting for sex, age group, ethnicity, housing group (fixed effects) and household (random effect), comparing those who had moved to East Village at follow-up with those who did not. Results Of 877 adults (69%) followed-up, 578 (66%) provided valid accelerometry and GPS data for at least 1 day (≥540 min) at both time points; half had moved to East Village. Despite no overall effects on physical activity levels, sizeable improvements in walkability and access to public transport in East Village resulted in decreased daily vehicle travel (8.3 mins, 95%CI 2.5,14.0), particularly in the intermediate housing group (9.6 mins, 95%CI 2.2,16.9), and increased underground travel (3.9 mins, 95%CI 1.2,6.5), more so in the market-rent group (11.5 mins, 95%CI 4.4,18.6). However, there were no effects on time spent walking or cycling

Is a perceived supportive physical environment important for self-reported leisure time physical activity among socioeconomically disadvantaged women with poor psychosocial characteristics? An observational study

Background Over the past decade, studies and public health interventions that target the physical environment as an avenue for promoting physical activity have increased in number. While it appears that a supportive physical environment has a role to play in promoting physical activity, social-ecological models emphasise the importance of considering other multiple levels of influence on behaviour, including individual (e.g. self-efficacy, intentions, enjoyment) and social (e.g. social support, access to childcare) factors (psychosocial factors). However, not everyone has these physical activity-promoting psychosocial characteristics; it remains unclear what contribution the environment makes to physical activity among these groups. This study aimed to examine the association between the perceived physical environment and self-reported leisure-time physical activity (LTPA) among women living in socioeconomically disadvantaged areas demonstrating different psychosocial characteristics.Methods In 2007&ndash;8, 3765 women (18&ndash;45&thinsp;years) randomly selected from low socioeconomic areas in Victoria, Australia, self-reported LTPA, and individual, social and physical environmental factors hypothesised within a social-ecological framework to influence LTPA. Psychosocial and environment scores were created. Associations between environment scores and categories of LTPA (overall and stratified by thirds of perceived environment scores) were examined using generalised ordered logistic regression.Results Women with medium and high perceived environment scores had 20-38% and 44-70% greater odds respectively of achieving higher levels of LTPA than women with low environment scores. When stratified by thirds of psychosocial factor scores, these associations were largely attenuated and mostly became non-significant. However, women with the lowest psychosocial scores but medium or high environment scores had 76% and 58% higher odds respectively of achieving &ge;120&thinsp;minutes/week (vs. &lt;120&thinsp;minutes/week) LTPA.Conclusions Acknowledging the cross-sectional study design, the findings suggest that a physical environment perceived to be supportive of physical activity might help women with less favourable psychosocial characteristics achieve moderate amounts of LTPA (i.e. &ge;120&thinsp;minutes/week). This study provides further support for research and public health interventions to target perceptions of the physical environment as a key component of strategies to promote physical activity.<br /

Health promotion programs related to the Athens 2004 Olympic and Para Olympic games

BACKGROUND: The Olympic Games constitute a first-class opportunity to promote athleticism and health messages. Little is known, however on the impact of Olympic Games on the development of health-promotion programs for the general population. Our objective was to identify and describe the population-based health-promotion programs implemented in relation to the Athens 2004 Olympic and Para Olympic Games. METHODS: A cross-sectional survey of all stakeholders of the Games, including the Athens 2004 Organizing Committee, all ministries of the Greek government, the National School of Public Health, all municipalities hosting Olympic events and all official private sponsors of the Games, was conducted after the conclusion of the Games. RESULTS: A total of 44 agencies were surveyed, 40 responded (91%), and ten (10) health-promotion programs were identified. Two programs were implemented by the Athens 2004 Organizing Committee, 2 from the Greek ministries, 2 from the National School of Public Health, 1 from municipalities, and 3 from official private sponsors of the Games. The total cost of the programs was estimated at 943,000 Euros; a relatively small fraction (0.08%) of the overall cost of the Games. CONCLUSION: Greece has made a small, however, significant step forward, on health promotion, in the context of the Olympic Games. The International Olympic Committee and the future hosting countries, including China, are encouraged to elaborate on this idea and offer the world a promising future for public health

Cohort profile: Examining Neighbourhood Activities in Built Living Environments in London: the ENABLE London-Olympic Park cohort.

PURPOSE: The Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) project is a natural experiment which aims to establish whether physical activity and other health behaviours show sustained changes among individuals and families relocating to East Village (formerly the London 2012 Olympics Athletes' Village), when compared with a control population living outside East Village throughout. PARTICIPANTS: Between January 2013 and December 2015, 1497 individuals from 1006 households were recruited and assessed (at baseline) (including 392 households seeking social housing, 421 seeking intermediate and 193 seeking market rent homes). The 2-year follow-up rate is 62% of households to date, of which 57% have moved to East Village. FINDINGS TO DATE: Assessments of physical activity (measured objectively using accelerometers) combined with Global Positioning System technology and Geographic Information System mapping of the local area are being used to characterise physical activity patterns and location among study participants and assess the attributes of the environments to which they are exposed. Assessments of body composition, based on weight, height and bioelectrical impedance, have been made and detailed participant questionnaires provide information on socioeconomic position, general health/health status, well-being, anxiety, depression, attitudes to leisure time activities and other personal, social and environmental influences on physical activity, including the use of recreational space and facilities in their residential neighbourhood. FUTURE PLANS: The main analyses will examine the changes in physical activity, health and well-being observed in the East Village group compared with controls and the influence of specific elements of the built environment on observed changes. The ENABLE London project exploits a unique opportunity to evaluate a 'natural experiment', provided by the building and rapid occupation of East Village. Findings from the study will be generalisable to other urban residential housing developments, and will help inform future evidence-based urban planning

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data.

BACKGROUND: Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. METHODS: The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. RESULTS: Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals' travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%). CONCLUSION: We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers