Neighborhood, Route, and School Environments and Children's Active Commuting

Panter, Jenna; Jones, Andrew; Sluijs, Esther M. F. van; Griffin, Simon J.

doi:10.1016/j.amepre.2009.10.040

Cited by 205 publications

(235 citation statements)

References 19 publications

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“…In the UK, a higher road density increased the odds of walking, while having a route that was more direct, negatively related to walking for 9-and 10-year-old children (n=2064) living in Norfolk County (Panter et al 2010). Children living in more deprived areas were also less likely to walk to school.…”

Section: Literature Reviewmentioning

confidence: 96%

“…More recently, studies have measured the environment using the shortest path between a child's home and school (Schlossberg et al 2006;Timperio et al 2006;Panter et al 2010;Panter et al 2011;Larsen, Gilliland, and Hess 2012). The shortest path may be an improvement over previous methods because its use assumes a conceptual move away from origin/destination environmental exposure only.…”

Section: Literature Reviewmentioning

confidence: 99%

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School travel route measurement and built environment effects in models of children’s school travel behavior

Larsen

Buliung

Faulkner

2015

JTLU

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Abstract:The most common form of physical activity for people of all ages is walking, thus the use of active travel modes, such as walking or cycling for school trips, can increase daily physical activity levels. School travel is one way to encourage walking and cycling on a daily basis. Much of the recent literature reports inconsistent results pertaining to how the built environment may relate to active school travel. To date, there is no consistent approach toward conceptualizing the "environment" for its measurement, and this may be partially to blame for the inconsistent results. The purpose of this paper is twofold: to examine how characteristics of the built environment might relate to mode of school travel, while testing how measurement of the environment may influence the results in terms of the shortest path or respondent reported route mapping. The results indicate that model parameter estimates vary when using these two route measurement methods. Differences in the conceptualization and measurement of the school travel environment could carry forward into misguided planning or policy interventions targeting environmental features that may actually have no influence on school travel decisions.

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Section: Literature Reviewmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

School travel route measurement and built environment effects in models of children’s school travel behavior

Larsen

Buliung

Faulkner

2015

JTLU

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“…cycle paths and trees) , and increased walkability, thus increasing physical activity (De Meester, van Dyck, Bourdeaudhuiji, Deforche & Cardon, 2012, De Meester et al, 2013Kimbro et al, 2011;Zhu & Lee, 2008). In others, low socio-economic status are associated with reduced physical activity (Bolte, Tamburlini & Kohlhuber, 2010;Griew, Page, Thomas, Hulsdon & Cooper, 2010;Pabayo, Belsky, Gauvin & Curtis, 2011;Panter, Jones, van Sluijs & Griffin, 2010;Spengler et al, 2011), the causes of which may in part relate to poorer access and provision of playground facilities.…”

Section: Introductionmentioning

confidence: 99%

Physical activity patterns of ethnic children from low socio-economic environments within the UK

Eyre

Duncan

Birch

et al. 2014

Journal of Sports Sciences

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Original citation:Eyre, E.L.J. , Duncan, M.J. , Birch, S. , Cox, V. and Blackett, M. (2014) Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. This copy may differ from the published PDF. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. CURVE is the Institutional Repository for Coventry University ABSTRACTMany children fail to meet physical activity (PA) guidelines for health benefits. PA behaviours are complex and depend on numerous interrelated factors. The study aims to develop current understanding of how children from low Socioeconomic environments within the UK, use their surrounding built environments for PA by using advanced technology. The environment was assessed in 96 school children (7 -9 years) using GPS monitoring (Garmin Forerunner, 305). In a subsample of 46 children, the environment and physical activity was assessed using an integrated GPS and heart rate monitor. The percentage of time spent indoor, outdoor, in green and non-green environments along with time spent in MVPA in indoor and outdoor environments were assessed. A 2-by-2 repeated measures ANCOVA, controlling for BMI, BF%, assessed environmental differences. The findings show that 42% of children from deprived wards of Coventry fail to meet PA guidelines, of which 43% was accumulated during school. Children engaged in more MVPA outdoor than indoor environments (p <0.01) and a greater amount of time was spent in non-green environments (p <0.01). Increased time outdoors was negatively associated with BF%. In conclusion, outdoor environments are important for health enhancing PA and reducing fatness in deprived and ethnic children.

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“…Los métodos de medición de la variable distancia en la literatura previa son variados, obteniendo la distancia de la ruta desde la casa hasta el colegio a través de cuestionarios autoadministrados (12,15), Sistemas de Información Geográfica (GIS) (13,15), dibujos de mapas de la ruta (16), Google Maps TM (17)(18)(19)(20) o sistemas de posicionamiento global (GPS) (21,22). La medición con GPS parece ser el método más preciso (23), pero el elevado coste de este material hace que en ocasiones sea difícil su acceso e implementación en las investigaciones (24).…”

Section: Introductionunclassified

Evaluación de la concordancia de dos métodos para determinar la distancia del desplazamiento activo al colegio en escolares

et al. 2016

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Objective: To analyze the agreement between two measuring methods (Google Maps TM vs GIS) for the prediction of the distance from home to school. Methods: A total of 542 scholars aged 8-11 years old (mean = 9.36 ± 0.6) from the South of Spain participated in the study, reporting their respective address family home. The distance from home to school was calculated using two different softwares: Google Maps TM and Geographic Information System, in route and straight line. The association between the two methods was analyzed using the Spearman correlation, and the agreement through the Intraclass Correlation Coeffi cient and the Bland Altman method. Results: The correlation between the two methods of measurement proposed was signifi cant (r = 0.966; p < 0.001; r = 0.984; p < 0.001, and r = 0.954; p < 0.001, respectively), and the agreement was excellent (ICC = 0.96, p < 0.001; ICC = 0.92; p < 0.001, ICC = 0,97; p < 0.001). Conclusions: Both measurement methods may be used depending on the needs of research, introducing both a high agreement. However, the use of Geographic Information System in route is recommended if it is funded, because it was demonstrated its reliability and validity. ResumenObjetivo: analizar la concordancia de dos métodos de medición (Google Maps TM vs. Sistema de Información Geográfi ca) para la determinación de la distancia desde el domicilio familiar hasta el colegio. Métodos: un total de 542 escolares de entre 8-11 años de edad (media = 9,36 ± 0,6) del sur de España participaron en el estudio, facilitando la dirección de su domicilio familiar. La distancia desde el domicilio familiar al colegio se calculó mediante la utilización de dos programas diferentes: Google Maps TM y Sistema de Información Geográfi ca (GIS) en ruta y en línea recta. La asociación entre ambos métodos fue analizada utilizando la correlación de Spearman y el grado de acuerdo a través del coefi ciente de correlación intraclase (ICC), así como el método Bland Altman. Resultados: la correlación entre ambos métodos de medición propuestos fue muy signifi cativa (r = 0,966, p < 0,001; r = 0,984, p < 0,001; y r = 0,954, p < 0,001, respectivamente), y la concordancia fue excelente (ICC = 0,96, p < 0,001; ICC = 0,92, p < 0,001; ICC = 0,97, p < 0,001). Conclusiones: los métodos de medición estudiados podrían ser utilizados en función de las necesidades de la investigación, al presentar ambos una alta concordancia. Sin embargo, se recomienda la utilización del Sistema de Información Geográfi ca en ruta si se cuenta con medios y fi nanciación, por tratarse de un método constatado en fi abilidad y validez.

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Neighborhood, Route, and School Environments and Children's Active Commuting

Cited by 205 publications

References 19 publications

School travel route measurement and built environment effects in models of children’s school travel behavior

School travel route measurement and built environment effects in models of children’s school travel behavior

Physical activity patterns of ethnic children from low socio-economic environments within the UK

Evaluación de la concordancia de dos métodos para determinar la distancia del desplazamiento activo al colegio en escolares

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