Increasing Young People’s Active Modes of Transport: An Urgent Review of the Child-friendliness of Multi-sector Policies Required

Invited Commentary on Correlates of Active School Transport Immediately Before and After the Transition from Primary to Secondary School: A Pilot Study

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

The relationship between destination proximity, destination mix and physical activity behaviors

Background. The presence and mix of destinations is an important aspect of the built environment that may encourage or discourage physical activity. This study examined the association between the proximity and mix of neighbourhood destinations and physical activity. Methods. Secondary analysis was undertaken on physical activity data from Western Australian adults (n=1394). These data were linked with geographical information systems (GIS) data including the presence and the mix of destinations located within 400 and 1500 m from respondents' homes. Associations with walking for transport and recreation and vigorous physical activity were examined. Results. Access to post boxes, bus stops, convenience stores, newsagencies, shopping malls, and transit stations within 400 m (OR 1.63–5.00) and schools, transit stations, newsagencies, convenience stores and shopping malls within 1500 m (OR 1.75–2.38) was associated with participation in regular transport-related walking. A dose–response relationship between the mix of destinations and walking for transport was also found. Each additional destination within 400 and 1500 m resulted in an additional 12 and 11 min/fortnight spent walking for transport, respectively. Conclusion. Proximity and mix of destinations appears strongly associated with walking for transport, but not walking for recreation or vigorous activity. Increasing the diversity of destinations may contribute to adults doing more transport-related walking and achieving recommended levels of physical activity

The effect of moving to East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village, on physical activity and adiposity (ENABLE London): a cohort study

Background The built environment can affect health behaviours, but longitudinal evidence is limited. We aimed to examine the effect of moving into East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village that was repurposed on active design principles, on adult physical activity and adiposity. Methods In this cohort study, we recruited adults seeking new accommodation in East Village and compared physical activity and built environment measures with these data in control participants who had not moved to East Village. At baseline and after 2 years, we objectively measured physical activity with accelerometry and adiposity with body-mass index and bioimpedance, and we assessed objective measures of and participants' perceptions of change in their built environment. We examined the change in physical activity and adiposity between the East Village and control groups, after adjusting for sex, age group, ethnicity, housing tenure, and household (as a random effect). Findings We recruited participants for baseline assessment between Jan 24, 2013, and Jan 7, 2016, and we followed up the cohort after 2 years, between Feb 24, 2015, and Oct 24, 2017. At baseline, 1819 households (one adult per household) consented to initial contact by the study team. 1278 adults (16 years and older) from 1006 (55%) households participated at baseline; of these participants, 877 (69%) adults from 710 (71%) households were assessed after 2 years, of whom 441 (50%) participants from 343 (48%) households had moved to East Village. We found no effect associated with moving to East Village on daily steps, the time spent doing moderate-to-vigorous physical activity (either in total or in 10-min bouts or more), daily sedentary time, body-mass index, or fat mass percentage between participants who had moved to East Village and those in the control group, despite sizeable improvements in walkability and neighbourhood perceptions of crime and quality among the East Village group relative to their original neighbourhood at baseline. Interpretation Despite large improvements in neighbourhood perceptions and walkability, we found no clear evidence that moving to East Village was associated with increased physical activity. Improving the built environment on its own might be insufficient to increase physical activity

Does getting a dog increase recreational walking?

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Tracking of pedometer-determined physical activity in adults who relocate: results from RESIDE

<p>Abstract</p> <p>Background</p> <p>This secondary analysis investigated the extent and pattern of one-year tracking of pedometer-determined physical activity in people who relocated within the same metropolitan area (T1: baseline and T2: post-relocation). Specifically, data were derived from the RESIDential Environment Project (RESIDE), a natural experiment of people moving into new housing developments.</p> <p>Methods</p> <p>1,175 participants (491 males, age = 42.6 ± 12.7 years, BMI = 27.2 ± 9.9 kg/m²; 684 females, age = 41.2 ± 11.3 years, BMI = 25.4 ± 5.2 kg/m²) wore a Yamax pedometer (SW-200-024) for seven days during the same season at both time points. Pearson's product-moment and Spearman's rank order correlations were used to evaluate the extent of tracking of mean steps/day. Age categories were set as youngest-29.9 (19 was the youngest in males, 20 in females), 30–39.9, 40–49.9, 50–59.9, and 60-oldest (78 was the oldest in males, 71 in females). Change in steps/day was also described categorically as: 1) stably inactive < 7,500 steps/day; 2) decreased activity (moved from ≥ 7,500 to < 7,500 steps/day between T1 and T2); 3) increased activity (moved from < 7,500 to ≥ 7,500 steps/day between T1 and T2); and, 4) stably active ≥ 7,500 steps/day at both time points. Stratified analyses were used to illuminate patterns by sex, age, and BMI-defined weight categories.</p> <p>Results</p> <p>Overall, there was a small (non-significant) decrease in steps/day between T1 and T2 (mean ± SD is -81 ± 3,090 with 95%CI -259 to 97). With few exceptions (i.e., older women), both Pearson's and Spearman's correlations were moderate (r = 0.30–0.59) to moderately high (r = 0.60–0.70). The relative change/stability in steps/day (cut at 7,500 steps/day) was not significant across age groups in males (χ²= 17.35, p = .137) but was in females (χ²= 50.00, p < .0001). In both males and females the differences across BMI categories was significant (χ²= 22.28, p = .001 and χ²= 15.70, p = .015, respectively). For both sexes, those in the obese category were more stably inactive (and less stably active) between assessment points compared with those who were categorized as normal weight.</p> <p>Conclusion</p> <p>Despite relocation, Western Australian adults held their rank position to a moderate to moderately high extent over one year. Categorized and expressed as relative stability/change over time, sex, age, and BMI patterns were evident.</p

Contribution of the physical environment to socioeconomic gradients in walking in the Whitehall II study

Socioeconomic gradients in walking are well documented but the underlying reasons remain unclear. We examined the contribution of objective measures of the physical environment at residence to socioeconomic gradients in walking in 3363 participants (50-74years) from the Whitehall II study (2002-2004). Individual-level socioeconomic position was measured as most recent employment grade. The contribution of multiple measures of the physical environment to socioeconomic position gradients in self-reported log transformed minutes walking/week was examined by linear regression. Objective measures of the physical environment contributed only to a small extent to socioeconomic gradients in walking in middle-aged and older adults living in Greater London, UK. Of these, only the number of killed and seriously injured road traffic casualties per km of road was predictive of walking. More walking in areas with high rates of road traffic casualties per km of road may signal an effect not of injury risk but of more central locations with multiple destinations within short distances ('compact neighbourhoods'). This has potential implications for urban planning to promote physical activity

Correlates of distances traveled to use recreational facilities for physical activity behaviors

BACKGROUND: Information regarding how far people are willing to travel to use destinations for different types of recreational physical activity behaviors is limited. This study examines the demographic characteristics, neighborhood opportunity and specific-physical activity behaviors associated with distances traveled to destinations used for recreational physical activity. METHODS: A secondary analysis was undertaken of data (n = 1006) from a survey of Western Australian adults. Road network distances between respondents' homes and 1) formal recreational facilities; 2) beaches and rivers; and 3) parks and ovals used for physical activity were determined. Associations between distances to destinations and demographic characteristics, neighborhood opportunity (number of destinations within 1600 meters of household), and physical activity behaviors were examined. RESULTS: Overall, 56.3% of respondents had used a formal recreational facility, 39.9% a beach or river, and 38.7% a park or oval. The mean distance traveled to all destinations used for physical activity was 5463 ± 5232 meters (m). Distances traveled to formal recreational facilities, beaches and rivers, and parks and ovals differed depending on the physical activity undertaken. Younger adults traveled further than older adults (7311.8 vs. 6012.6 m, p = 0.03) to use beaches and rivers as did residents of socio-economically disadvantaged areas compared with those in advantaged areas (8118.0 vs. 7311.8 m, p = 0.02). Club members traveled further than non-members to use parks and ovals (4156.3 vs. 3351.6 meters, p = 0.02). The type of physical activity undertaken at a destination and number of neighborhood opportunities were also associated with distance traveled for all destination types. CONCLUSION: The distances adults travel to a recreational facility depends on the demographic characteristics, destination type, physical activity behavior undertaken at that destination, and number of neighborhood opportunities. Knowing how far adults travel to undertake physical activity will assist in designing supportive neighborhoods and designing future ecological research

A cross-sectional study of the individual, social, and built environmental correlates of pedometer-based physical activity among elementary school children

Background: Children who participate in regular physical activity obtain health benefits. Preliminary pedometerbased cut-points representing sufficient levels of physical activity among youth have been established; however limited evidence regarding correlates of achieving these cut-points exists. The purpose of this study was to identify correlates of pedometer-based cut-points among elementary school-aged children.Method: A cross-section of children in grades 5-7 (10-12 years of age) were randomly selected from the most (n = 13) and least (n = 12) &lsquo;walkable&rsquo; public elementary schools (Perth, Western Australia), stratified by socioeconomic status. Children (n = 1480; response rate = 56.6%) and parents (n = 1332; response rate = 88.8%) completed a survey, and steps were collected from children using pedometers. Pedometer data were categorized to reflect the sex-specific pedometer-based cut-points of &ge;15000 steps/day for boys and &ge;12000 steps/day for girls. Associations between socio-demographic characteristics, sedentary and active leisure-time behavior, independent mobility, active transportation and built environmental variables - collected from the child and parent surveys - and meeting pedometer-based cut-points were estimated (odds ratios: OR) using generalized estimating equations.Results: Overall 927 children participated in all components of the study and provided complete data. On average, children took 11407 &plusmn; 3136 steps/day (boys: 12270 &plusmn; 3350 vs. girls: 10681 &plusmn; 2745 steps/day; p &lt; 0.001) and 25.9% (boys: 19.1 vs. girls: 31.6%; p &lt; 0.001) achieved the pedometer-based cut-points. After adjusting for all other variables and school clustering, meeting the pedometer-based cut-points was negatively associated (p &lt; 0.05) with being male (OR = 0.42), parent self-reported number of different destinations in the neighborhood (OR 0.93), and a friend&rsquo;s (OR 0.62) or relative&rsquo;s (OR 0.44, boys only) house being at least a 10-minute walk from home. Achieving the pedometer-based cut-points was positively associated with participating in screen-time &lt; 2 hours/day (OR 1.88), not being driven to school (OR 1.48), attending a school located in a high SES neighborhood (OR 1.33), the average number of steps among children within the respondent&rsquo;s grade (for each 500 step/day increase: OR 1.29), and living further than a 10-minute walk from a relative&rsquo;s house (OR 1.69, girls only).Conclusions: Comprehensive multi-level interventions that reduce screen-time, encourage active travel to/from school and foster a physically active classroom culture might encourage more physical activity among children.<br /