To investigate the demographic, physical activity, and route characteristics related to children's school trips.
Design.
Cross-sectional exploratory study.
Setting.
Eighteen elementary schools in the Austin Independent School District, Austin, Texas.
Subjects.
One hundred twelve children aged 7 to 12 years.
Measures.
Accelerometer and Global Positioning System (GPS) devices provided objective measures of school travel and physical activity. Parental survey (response rate = 34.2%) provided children's demographic and household information.
Analysis.
Generalized linear regression analyses were used for unadjusted and adjusted models estimating correlates of total moderate to vigorous physical activity (MVPA) and school trip–related MVPA's contribution rate.
Results.
Walking trips were .44 miles (.71 km) on average. Those who walked to school had about 11 more minutes of daily MVPA than nonwalkers (35.03 vs. 24.06) and higher proportions of their daily MVPA obtained from school commute trips (21.78% vs. 2.41%). School trips accounted for 11.2% of total daily MVPA on average, 12.9% for those who met the physical activity recommendation, and 35.2% for the sedentary children who belonged to the lowest MVPA quartile.
Conclusion.
Active school commuting appears to be a valuable means to promote physical activity, and its contributions toward total physical activity vary across different demographic groups and community settings. Objective and detailed data from GPS and accelerometer units can facilitate the assessment of route/trip characteristics and physical activity implications of school transportation.
OgdenCLCarrollMDCurtinLR. Prevalence of high body mass index in US children and adolescents, 2007–2008. JAMA. 2010;303:242–249.
2.
Centers for Disease Control and Prevention. Barriers to children walking to or from school – United States, 2004. MMWR Morb Mortal Wkly Rep.2005;54:949–952.
3.
McDonaldNC. Active transportation to school: Trends among US schoolchildren, 1969–2001. Am J Prev Med.2007;32:509–516.
4.
MartinSCarlsonS. Barriers to children walking to or from school—United States, 2004 [reprinted from MMWR. 2005;54:949–952]. JAMA. 2005;294:2160–2162.
5.
McDonaldNCDeakinEAalborgAE. Influence of the social environment on children's school travel. Prev Med.2010;50:S65–S68.
6.
SalmonJSalmonLCrawfordDA. Associations among individual, social, and environmental barriers and children's walking or cycling to school. Am J Health Promot. 2007;22:107–113.
7.
RodríguezAVogtCA. Demographic, environmental, access, and attitude factors that influence walking to school by elementary school-aged children. J Sch Health. 2009;79:255–261.
8.
LeeCZhuXYoonJVarniJ. Beyond distance: Children's school travel mode choice. Ann Behav Med.2013;45:55–67.
9.
PanterJRJonesAPvan SluijsEMGriffinSJ. Attitudes, social support and environmental perceptions as predictors of active commuting behaviour in school children. J Epidemiol Community Health. 2010;64:41–48.
10.
TimperioABallKSalmonJ. Personal, family, social, and environmental correlates of active commuting to school. Am J Prev Med.2006;30:45–51.
11.
McDonaldNC. Travel and the social environment: Evidence from Alameda County, California. Transp Res D Transp Environ. 2007;12:53–63.
12.
McMillanTE. The relative influence of urban form on a child's travel mode to school. Transp Res A Policy Pract. 2007;41:69–79.
13.
McDonaldNCBrownALMarchettiLMPedrosoMS. U.S. school travel, 2009 an assessment of trends. Am J Prev Med.2011;41:146–151.
14.
CooperARPageASWheelerBW. Mapping the walk to school using accelerometry combined with a global positioning system. Am J Prev Med.2010;38:178–183.
15.
WebberSCPorterMM. Monitoring mobility in older adults using global positioning system (GPS) watches and accelerometers: A feasibility study. J Aging Phys Act. 2009;17:455–467.
16.
WuJJiangCLiuZHoustonD. Performances of different global positioning system devices for time-location tracking in air pollution epidemiological studies. Environ Health Insights. 2010;4:93–108.
17.
WietersKMKimJHLeeC. Assessment of Wearable global positioning system units for physical activity research. J Phys Act Health. 2012;9:913–923.
18.
SmithGGidlowCDaveyRFosterC. Research What is my walking neighbourhood? A pilot study of English adults' definitions of their local walking neighbourhoods. Int J Behav Nutr Phys Act. 2010;7:34–41.
19.
CooperARPageASWheelerBW. Patterns of GPS measured time outdoors after school and objective physical activity in English children: The PEACH project. Int J Behav Nutr Phys Act. 2010;7:31–39.
20.
WheelerBWCooperARPageASJagoR. Greenspace and children's physical activity: A GPS/GIS analysis of the PEACH project. Prev Med.2010;51:148–152.
21.
OreskovicNMBlossomJFieldAE. Combining global positioning system and accelerometer data to determine the locations of physical activity in children. Geospat Health. 2012;6:263–272.
22.
RodriguezDAChoGHElderJP. Identifying walking trips from IGPS and accelerometer data in adolescent females. J Phys Act Health. 2012;9:421–431.
23.
HusseyJBellCGormleyJ. The measurement of physical activity in children. Phys Ther Rev.2007;12:52–58.
SrinivasanKKPradhanGNNaiduGM. Commute mode choice in a developing country—role of subjective factors and variations in responsiveness across captive, semicaptive, and choice segments. Transp Res Rec.2007;2038:53–61.
26.
FilloneAMMontalboCMTiglaoNC. Assessing urban travel: A structural equations modeling (SEM) approach. Proc East Asia Soc Transp Stud.2005;5:1050–1064.
27.
BeimbornEAGreenwaldMJJinX. Accessibility, connectivity, and captivity—impacts on transit choice. Transit Plann Dev Manage Perform Mark Fare Policy Intermodal Transf Facil. 2003;1835:1–9.
28.
EarlPEPottsJ. Latent demand and the browsing shopper. Managerial Decis Econ.2000;21:111–122.
29.
WilliamsPWBasfordR. Segmenting downhill skiing's latent demand markets. Am Behav Sci.1992;36:222–235.
30.
AssakerGVinziVEO'ConnorP. Extending a tourism causality network model: A cross-country, multigroup empirical analysis. Tourism Hospitality Res.2011;11:258–277.
31.
LandisBW. Bicycle system performance measures: The interaction hazard and latent demand score models. ITE J.1996;66:18–26.
32.
MoudonAVHessPMMatlickJMPergakesN. Pedestrian location identification tools: Identifying suburban areas with potentially high latent demand for pedestrian travel. Transp Res Rec.2002;1818:94–101.
33.
EttemaD. Latent activities: Modeling the relationship between travel times and activity participation. Transp Res Rec.2005;1926:171–180.
34.
CerveroR. Induced travel demand: Research design, empirical evidence, and normative policies. J Plann Lit.2002;17:3–20.
35.
AlmanzaEJerrettMDuntonG. A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. Health Place. 2012;18:46–54.
36.
Tudor-LockeCCamhiSMTroianoRP. A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and Nutrition Examination Survey, 2003–2006. Prev Chronic Dis.2012;9:E113.
37.
TrostSGLoprinziPDMooreRPfeifferKA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43:1360–1368.
38.
CrouterSEHortonMBassettDR. Use of a two-regression model for estimating energy expenditure in children. Med Sci Sports Exerc. 2012;44:1177–1185.
39.
HarrellJSMcMurrayRGBaggettCD. Energy costs of physical activities in children and adolescents. Med Sci Sports Exerc. 2005;37:329–336.
40.
DuntonGLiaoYAlmanzaEJerrettM. Locations of joint physical activity in parent–child pairs based on accelerometer and GPS monitoring. Ann Behav Med.2013;45:162–172.
41.
FreedsonPPoberDJanzKF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005;37:S523–S530.
42.
StrongWBMalinaRMBlimkieCJR. Evidence based physical activity for school-age youth. J Pediatr. 2005;146:732–737.
43.
BlaesABaquetGFabreC. Is there any relationship between physical activity level and patterns, and physical performance in children?Int J Behav Nutr Phys Act. 2011;8:122.
44.
LoprinziPDLeeHCardinalBJ. The relationship of Actigraph Accelerometer cut-points for estimating physical activity with selected health outcomes: Results from NHANES 2003–06. Res Q Exerc Sport. 2012;83:422–430.
45.
StoneMRRowlandsAVMiddlebrookeAR. The pattern of physical activity in relation to health outcomes in boys. Int J Pediatr Obes.2009;4:306–315.
46.
Gordon-LarsenPNelsonMCPagePPopkinBM. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics. 2006;117:417–424.
47.
FernandesMSturmR. Facility provision in elementary schools: Correlates with physical education, recess, and obesity. Prev Med.2010;50:S30–S35.
48.
SchlossbergMGreeneJPhillipsPP. School trips: Effects of urban form and distance on travel mode. J Am Plann Assoc. 2006;72:337–346.
49.
ZhuXLeeCLuZYuC. Threshold and impacts of walkable distance for active school transportation in different contexts. Paper presented at: Robert Wood Johnson Foundation Active Living Research; 2012; San Diego, Calif.
50.
PontKZivianiJWadleyD. Environmental correlates of children's active transportation: A systematic literature review. Health Place. 2009;15:849–862.
51.
CooperARPageASFosterLJQahwajiD. Commuting to school: Are children who walk more physically active?Am J Prev Med.2003;25:273–276.
52.
FaulknerGEBuliungRNFloraPKFuscoC. Active school transport, physical activity levels and body weight of children and youth: A systematic review. Prev Med.2009;48:3–8.
