Is the physical activity environment surrounding primary schools associated with students’ weight status, physical activity or active transport, in regional areas of Victoria, Australia? A cross-sectional study

Abstract

ObjectivesTo explore whether the physical activity (PA) environment (walkability, greenspace and recreational facilities) surrounding regional primary schools is associated with children’s PA levels, active transport and weight status. Limited research on this topic has been conducted outside of major cities.DesignCross-sectional ecological study using baseline data from two large-scale obesity prevention interventions.SettingEighty (n=80) primary schools across two regional areas in Victoria, Australia.ParticipantsStudents aged 8–13 years (n=2144) attending participating primary schools.Outcome measuresMeasured weight status (body mass index z-score, proportion overweight/obese) and self-reported PA behaviours (meeting PA recommendations and active travel behaviour).ResultsWhen adjusted for student and school demographics, students had significantly increased odds of using active transport to or from school when the school neighbourhood was more walkable (OR 1.21 (95% CI 1.09 to 1.35), had a greater number of greenspaces (OR 1.35 (95% CI 1.20 to 1.53)) and a greater number of recreational facilities (OR 1.18 (95% CI 1.07 to 1.31)). A higher cumulative PA environment score was also associated with a higher proportion of children using active transport (OR 1.33 (95% CI 1.28 to 1.51)). There were no significant associations between the PA environment measures and either weight status or meeting the PA recommendations in adjusted models.ConclusionsThis study is the first of its kind exploring school neighbourhood environments and child weight status and PA in regional areas of Australia. It highlights the potential of the environment surrounding primary schools in contributing to students’ active travel to and from school. Further research with the use of objective PA measurement is warranted in regional areas that have been under-researched.Trial registration numberAustralian New Zealand Clinical Trials Registry (ANZCTR.org.au) identifier 12616000980437; Results.

A Review of the Role of Built Environment and Temperature in the Development of Childhood Obesity

“…Other relevant potential reasons for obtaining variable results could be, as Jacobs et al pointed out, the lack of a genuine association or insufficient power to detect differences [36]. Moreover, different study designs could also have led to different results.…”

“…Full-text screening: Thereafter, 22 potential articles remained, full texts of which were retrieved and read. Of these, 11 were excluded as they did not conform to the PICOST eligibility criteria [32][33][34][35][36][37][38][39][40][41][42]. For example, some studies were excluded because the population consisted of a wider childhood age range not conforming to our age group of interest despite the allowed margin of one year [34,39,42].…”

“…Others were excluded as they especially studied children having a higher risk of obesity [35,40]. Exposure to school environment, either within-school environment or the neighbourhood around schools, was studied in three articles; hence, these were excluded [32,36,38]. The studies by Barnett et association of built environment with obesity as an outcome [33,37,41].…”

“…After title and abstract screening, 22 potential articles remained for full texts screening. Of these, 11 articles were excluded as they did not conform to the PICOST eligibility criteria [32][33][34][35][36][37][38][39][40][41][42]. Reasons for exclusion are provided within the detailed methods in the supplementary material, Appendix B.…”

Active School Commuting in School Children: A Narrative Review of Current Evidence and Future Research Implications

“…Prior to the advent of GISs, time-and resource-intensive on-site auditing was the primary method for obtaining such information [126]. In recent years, spatial datasets of street networks, land use information, and locations of schools and physical activity facilities have become more accessible from government and non-profit organisations alongside the rapid advancement of a variety of GIS software which has increased the processing and analysis of these data drastically [153]. Furthermore, these spatial datasets can be combined with area-or neighbourhood-level data from other sources, such as census data, to generate secondary spatial datasets that capture crucial factors found to be positively associated with ASC [126].…”