Young people who are physically active are less likely to be overweight (22,27) and have better cardiovascular risk factor profiles (1,2) and other indicators of health (22,34) than those who are less active. However, population surveys show that many young people do not meet the recommendation of an hour of moderate-to-vigorous intensity physical activity (MVPA) every day (15,20). Similarly, studies from Europe and the United States using objective measurement of physical activity with accelerometers have shown that total physical activity and MVPA is lower in adolescents than that in children (21,26,32,36). Cohort studies have shown that levels in the same children decline throughout childhood (21,26). These studies describe a substantial decline in daily MVPA throughout adolescence, and there is thus a need to identify ways in which youth physical activity may be increased, or at least maintained at childhood levels, throughout adolescence.
Relatively little is known about the factors that are associated with the decline in physical activity throughout adolescence (10). Although factors such as puberty and maturation may play an important role, one area for further investigation is the potential for key life events, such as the transition from primary to secondary school, to influence behavior. Moving school is often associated with changes in peer groups and environment and may be a critical point in the life of early adolescents, but little is known about how this transition may influence physical activity behavior.
Active travel to school has been associated with higher daily levels of physical activity and higher cardiorespiratory fitness in children and adolescents (12,17,23,24), and the UK government now puts walking and cycling at the heart of its transport and health policies (14). The mode of travel to school changes for many children across the transition from primary to secondary school, with fewer children walking to school and more using motorized transport (13). Since the average length of the trip to primary school in the United Kingdom is 1.5 miles (2.4 km), increasing to 3.5 miles (5.6 km) at secondary school (13), the change in travel mode could be a function of distance to school.
Several studies have shown that distance between home and school is one of the most important and consistent predictors of active commuting to school, with longer distance associated with lower rates of active travel (11,25,29,35). Home-to-school distance has also been shown to be important for children’s physical activity, with increasing distance associated with higher levels of daily MVPA (30,38) in children who walk to school but not in those using motorized transport. The results from these cross-sectional studies suggest that a switch in school travel mode may be associated with change in physical activity but could not demonstrate this. The purpose of the present study was to explore whether changes in travel mode to school when moving from primary to secondary school were associated with change in physical activity in a cohort of British children.
The initial sample was 1307 children recruited for the Personal and Environmental Associations with Children’s Health (PEACH) project, a longitudinal study investigating the environmental and personal determinants of physical activity in children across the transition from primary to secondary school. Children in their last year of primary school (year 6, 11.0 ± 0.4 yr) were recruited from 23 state schools within a UK city. The primary schools were selected as those with transition rates >40% to one of eight urban state-funded secondary schools that were chosen to be representative of the city on the basis of geographic location and the Index of Multiple Deprivation (IMD). Only one primary school of those approached declined to participate in the study. Of 1899 children invited to take part, 1340 provided parental consent (70.5%) and 1307 were present in school on baseline measurement days. Nine hundred and fifty-three children (72.9%) were followed up 1 yr later in their first year of secondary school. A University Ethics Committee approved the study, and written informed consent was obtained from a parent/guardian of all participating children. Data were collected between September 2006 and July 2009.
Physical activity was measured for 7 d using an accelerometer (GT1M; ActiGraph LLC, Pensacola, FL) worn on a belt around the waist, except when swimming, bathing, and sleeping. Height (m) was measured in bare or stockinged feet with the child standing upright against a transportable Harpenden stadiometer, and weight was measured to the nearest 0.1 kg using digital scales (SECA, Hamburg, Germany) with children wearing light indoor clothing. Body mass index (BMI) was calculated as weight (kg)/(height (m))2, and BMI SD score was derived from standard tables (5). A computerized questionnaire was used to describe self-reported child travel mode to and from school and pubertal stage. Travel mode to/from school was captured (two questions) as “How do you usually travel to (alternatively from) school?” (response options: by car or motorcycle, by bus, by bicycle, or by foot). Only children who traveled to school and home by the same method were included in the analyses. Pubertal status was measured using the scale developed by Petersen et al. (31), and five derived stages (equivalent to Tanner stages) were used in analyses. Minutes of daylight from 3 p.m. until sunset were used as an indicator of available daylight after school and were determined from standard tables. Street network distances between home and school (km) were calculated in a geographic information system (GIS; ArcGIS 9.2; Esri, Redlands, CA) for each participant from grid references derived from home and school postcodes using the National Statistics Postcode Directory. The UK IMD 2007 score based on full home postcode was used as an index of neighborhood deprivation for each child.
The ActiGraphs were set to record data every 10 s. Raw accelerometer files were downloaded using ActiLife v1.0.52 (ActiGraph LLC), and all data processing was carried out using KineSoft software (v3.3.62; KineSoft, Saskatchewan, Canada). A valid day was defined as recording at least 8 h of measurement between 7 a.m. and midnight, excluding periods of ≥60 min of zero values (allowing for up to two 1-min interruptions). For inclusion in analyses, participants were required to record three or more valid weekdays of data. Weekend data were not included. Time spent in MVPA was computed as the average number of minutes per valid day using a cut point of 2295 cpm to define MVPA (37). MVPA was adjusted for accelerometer wear time by dividing the total minutes of MVPA for each participant by his or her minutes of wear. This was then multiplied by the mean wear time of the appropriate sex separately at both measurement time points.
ANOVA with Bonferroni post hoc comparison where appropriate was used to investigate cross-sectional differences in MVPA between travel modes. Paired sample t-tests were used to investigate differences in mean values of MVPA between primary and secondary school. Longitudinal associations between change in travel mode between primary and secondary school and MVPA were explored using linear regression models with MVPA at follow-up as the outcome and with change in travel mode from walking to car travel as the exposure (with walk–walk as the reference category). All models were adjusted for potential confounders (baseline MVPA, age, BMI SD score and pubertal status, sex, IMD score, and daylight), and because participants were nested within schools, all models included school as a random effect. Analyses were performed using the XTREG command in Stata, and all data were analyzed using Stata/IC v10.1 in 2011/12 (StataCorp, LP, College Station, TX).
Children were measured in their final year of primary school and again in their first year of secondary school at the same time of year (median, 393 days later). At baseline, 1266 children provided accelerometer data, of whom 1104 met the accelerometer inclusion criteria. Seven children did not complete the computer questionnaire. At follow-up, 900 children provided accelerometer data with 683 meeting the inclusion criteria, and all but one completed the questionnaire. Six hundred and forty-four children provided valid accelerometer and questionnaire data at both time points. Of these, 565 used the same travel mode to and from primary school and 570 to and from secondary school. The final longitudinal sample was 500 children. Descriptive characteristics (age, height, weight, and BMI) and physical activity for the participants in the final sample did not differ from excluded participants. The accelerometer was worn for 745.0 ± 78.0 min·d−1 in primary school and 763.2 ± 92.2 min·d−1 in secondary school.
Approximately three-quarters (77.0%) of primary school children walked to/from school, with 19.1% traveling by car, 3.2% cycling, and 0.7% traveling by bus. One year later, in secondary school, the proportion of children walking to/from school had declined to 60.7%, car use and cycling were similar at 17.2% and 3.5%, respectively, but bus use had increased to 18.6%. Physical activity levels of children using the different modes of travel to school are shown in Table 1. In primary school, children who walked to/from school recorded 9.7 more minutes (95% confidence interval (CI), 5.2–14.3, P < 0.001) of MVPA each day than children traveling by car. These differences were greater in secondary school, with walkers recording 18.0 more minutes of MVPA (95% CI, 12.9–23.1, P < 0.001) than car travelers. Children who traveled by bus in secondary school recorded less MVPA than those traveling by foot (difference walk vs. bus = 13.3 min (95% CI, 8.4–18.2), P < 0.001) but more than those traveling by car (difference car vs. bus = 4.7 min (95% CI, −10.4 to 0.9), P = 0.102), although CIs just included zero. Values for cyclists were highly variable and were excluded from further analyses because of the small number of cyclists and the inability of accelerometers worn on the waist to accurately measure physical activity during cycling. Plots of hourly MVPA (Fig. 1) indicated that the major differences in physical activity between travel groups occurred when the journey to school (8 a.m. to 9 a.m.) or home from school (3 p.m. to 5 p.m.) was undertaken. Because all children travel to school between 8 a.m. and 9 a.m., physical activity in this hour was examined to better quantify the contribution of the school journey to daily MVPA. Children traveling by foot recorded 12.0% (7.2 ± 3.8 min) of their total daily MVPA between 8 a.m. and 9 a.m. in primary school and 17.8% of daily MVPA (12.1 ± 6.0 min) in secondary school. Values differed little between the sexes (primary: boy vs. girl, 7.7 ± 4.1 vs. 6.9 ± 3.5 min, P = 0.021; secondary: 12.2 ± 6.0 vs. 12.0 ± 6.0 min, P = 0.766). In contrast, car travelers recorded 6.9% (3.6 ± 2.6 min) and 9.1% (4.6 ± 2.8 min) of total daily MVPA (for primary and secondary school children, respectively) in this period. The difference between these groups suggests that walking to and from school contributes approximately 10% of daily MVPA in primary-school-age children and 18% in secondary school. No differences in MVPA were found between car users or walkers during the school day (9 a.m. to 3 p.m.) in both primary school and secondary school. After school (4 p.m. to 10 p.m.), walkers recorded slightly more MVPA each hour (primary: walk vs. car, 4.2 ± 2.4 vs. 3.4 ± 1.7 min, P = 0.001; secondary: 3.7 ± 2.4 vs. 3.2 ± 1.9 min, P = 0.040) than car travelers.
Longitudinal changes in physical activity by travel mode
Two hundred and sixty-six children (53.2%) walked to school in both primary and secondary school (walk–walk), and 39 (7.8%) traveled by car at both time points (car–car). The major changes in travel mode were children who walked to primary school changing to car (walk–car, n = 42 (8.4%)) or bus (walk–bus, n = 73 (14.6%)) travel to secondary school. Twenty (4.0%) of the children who traveled by car in primary school changed to bus travel at secondary school, with a further 29 (5.8%) adopting walking. The remaining 31 children used a range of other travel combinations and are not described. Changes in physical activity associated with changing mode of travel between primary and secondary school are shown in Table 2. There was a slight increase (2.5 min (4.1%)) in MVPA overall between primary and secondary school. However, children who walked both to primary and secondary school increased daily MVPA by 11.4% (P < 0.001), whereas in contrast, MVPA decreased by 15.5% (P = 0.003) in those who changed from walking to car travel, a net difference of approximately 18 min of MVPA (25% of walker values). Similarly, a change from car travel to walking was associated with 16.1% more daily MVPA (P = 0.038). In longitudinal regression models, changing from walking to car travel was associated with a reduction in MVPA compared with continuing to walk to school after adjustment for potential confounding factors (B (95% CI) = −9.1 (−15.9 to −2.4), P = 0.008).
Distance between home and school is a likely determinant of choice of travel mode. In primary school, 73.1% of children lived within 1 km of the school and 93.5% within 2.5 km, whereas in secondary school, only 16.2% of pupils lived within 1 km of the school and 65.6% within 2.5 km. Analyzed by travel mode, distance to school increased substantially between primary and secondary school in all groups except those who changed from car travel to walking to school where distance decreased (Table 3). In particular, distances were substantially greater where motorized transport was adopted or maintained at follow-up. At distances of up to 2.5 km, most children (59.3%) walked (Table 4), with only 6.3% using motorized transport. Above 2.5 km, car and bus travel predominated, and in particular, bus travel was used primarily for longer journeys. The mean distance walked to primary school was approximately two-thirds of a kilometer. As the change in distance between home and primary and secondary school increased, the frequency of walking to secondary school declined up to approximately 2.5 km, after which virtually no children walked to school (Table 4). A small proportion of children traveled by car or bus in all categories, although bus travel was predominantly used by those living over 3 km away. In longitudinal analyses, greater increases in home–school distance were associated with higher daily MVPA for children who walked to school but not for those who were driven (Fig. 2).
This study investigated whether a change in mode of travel to school between primary and secondary school was associated with change in daily levels of MVPA. In the sample overall, there was a small increase in daily MVPA between primary and secondary school. However, within groups defined by change in travel mode between primary and secondary school, larger positive and negative differences in MVPA were seen. MVPA increased across the primary–secondary transition in children who walked to school at both time points, likely because of the greater distances walked to secondary school. In contrast, children who changed from walking to school to traveling by car had substantially lower MVPA in secondary school than at primary. Adoption of bus travel was associated with a smaller reduction in MVPA (not statistically significant), whereas a change from car travel at primary school to walking at secondary increased daily MVPA.
In common with most studies using objective measurement of physical activity to explore the effect of active travel to school (17), cross-sectional analyses showed that children who walked to school recorded more daily MVPA than those using motorized transport. Analyses of longitudinal data extended these observations by showing that a change in travel mode to school can have a substantial influence on children’s physical activity. These data confirm the role of active travel in contributing to children’s overall activity and suggest that interventions to increase levels of active travel should also increase daily physical activity. It was notable that there was no significant difference in physical activity during the school day between travel groups, indicating that no physical activity compensation took place in response to the active journey. However, similar to a previous study (6), children who walked home from school were more active during the evening than those who traveled by car or bus, suggesting that active travel may encourage more physical activity than can be ascribed to the journey alone. The source of this potential activity remains to be described, but it may possibly be a result of greater time spent out of doors in active play during or after the journey. This concept of activity synergy, where participation in one activity promotes increased MVPA at other times, has recently been described in a sample of British children where higher weekday nonschool active travel predicted greater MVPA at other times (19).
The transition from primary to secondary school is a key life event at which it is likely that behavior patterns are formed that may be influential in later life. Earlier research showed that beliefs about the value of physical activity changed across this transition but were not associated with any difference in self-reported physical activity (18). Little is known about objectively measured physical activity across this transition because studies that have reported a decline in objectively measured MVPA between primary school-age children and adolescents have not described change specifically across the school transition. For example, in a cohort of 1032 North American children, objectively measured weekday MVPA fell progressively from approximately 3 h·d−1 at the age of 9 yr through 2 h·d−1 at the age of 11 yr and 1.5 h·d−1 at the age of 12 yr to 49 min·d−1 at the age of 15 yr (26). Although these ages span the transitions between elementary school (5–12 yr), junior high (12–14 yr), and high school (14–18 yr), no explicit linkage with school stage was made. Similarly in Danish children, MVPA declined from 45 min·d−1 at the age of 9 yr to 35 min·d−1 6 yr later (21), although the acute change between schools was not explored. The data from the present study suggest that overall MVPA changes little over the transition, but that changes in travel mode as a determinant of MVPA should be considered in longitudinal analyses. This is supported in the Danish study where adoption of cycling to school between baseline and follow-up measures was associated with a 9% increase in cardiorespiratory fitness compared with noncyclists (8). Although this change is presumably associated with an increase in MVPA or vigorous physical activity, there were insufficient data to describe the longitudinal changes in MVPA associated with change in travel mode in this study. Further studies are thus required to describe changes in physical activity that may be associated with school transition and to identify factors that may be associated with increases or decreases in physical activity.
To support interventions that encourage active travel, it is helpful to more precisely quantify the contribution that the journey to/from school can make to overall MVPA. We investigated the period between 8 a.m. and 9 a.m. when most children travel to school, because our previous studies (7) and unpublished data show that the journey to school is the predominant contributor to MVPA in this period. In the present study, primary children accumulated 7.7 min of MVPA in this time, contributing 11.6% of daily totals. In secondary school, this had increased to 12.1 min, 17.6% of total daily MVPA for these children. In contrast, car users accumulated 3.8 and 5.0 min of MVPA, respectively, and net differences suggest that the journeys to and from school can be estimated to contribute at least 10%–20% (8–14 min) of total daily MVPA. These values are similar to those reported by previous studies, albeit using different accelerometer thresholds to define MVPA (17,33). Between 8 a.m. and 9 a.m., there was no difference in MVPA between the sexes, suggesting that walking to school can be an equally effective contribution to volume of MVPA for both boys and girls. However, because girls are less active than the boys, this is potentially a greater contribution to overall daily values than for boys. Physical activity declines through adolescence, as do rates of active commuting (28), and as active travel appears to be a substantial contributor to overall MVPA in adolescence, interventions designed to prevent dropout from active travel could be an important approach to maintaining physical activity, especially in girls.
The increase in distance between home and school was strongly associated with change of travel mode and hence MVPA. In primary school, most children lived fairly close to their school (median home–school distance, 744 m), and we found no association between distance traveled and MVPA. However, in secondary school, distances were approximately three times greater, and most children who lived over 2.5 km from school used motorized transport. Change in distance was also associated with change in travel mode, with most children who had a relatively small increase in distance (<2 km) continuing to walk, whereas those moving to schools farther away traveled by car or bus. These data are similar to other studies, where household distance from school has been shown to be the strongest predictor of active transportation to school among children (12,29). Australian children were more likely to actively commute to school if they lived within 800 m (35), with the number of children not using active transport doubling with an increase of distance from 750 to 1500 m (25), and in Belgium, D’Haese et al. (11) showed that the farther 11- to 12-yr-old children lived from school, the less likely they were to actively commute by foot or bicycle, with a criterion distance for walking of 1.5 km. Our data suggest that a longer distance of 2.5 to 3 km may be the maximum acceptable walkable distance for children of this age, although the reasons underlying this discrepancy in walkable distance remain to be described. Two other cross-sectional UK studies have explored the role of distance in the association between walking and MVPA, and similar to the present study, these showed that the strength of association increased with greater walked distance (30,38).
This study used a large sample of children recruited from a predominantly urban area. The strengths of the study include the use of a longitudinal design and objective measurement of physical activity. However, there are several possible limitations. Travel mode to school was self-reported, which may result in some misclassification, although it is likely that by including children who reported the same mode to and from school, this is minimized. Although the original study sample was chosen to be representative of the city, substantially, more children walked to school in this study than national values (13) (73% vs. 50% in primary and 55% vs. 38% in secondary), with proportionately fewer traveling by motorized transport. The distance between home and school were derived using GIS and were shorter than national averages for both primary and secondary pupils, possibly explaining the higher proportion of children walking. Although we found a linear association between distance walked and physical activity, the GIS-computed distances may not necessarily have represented the actual route (and thus distance) taken. However, in other studies where comparisons between GIS computed routes and actual routes taken (measured with GPS) have been made, computed and actual distances are comparable, although the actual routes taken may differ (3,16). We have not investigated that in the present article, although ongoing analyses of GPS recordings of the children described in this study demonstrate that the route chosen to walk to school is generally direct. Thus, we would consider that the GIS-computed route is likely to be a realistic approximation of the true journey distance. A further limitation is that we did not recruit children from rural areas, and we are thus unable to describe the association between travel mode and physical activity in nonurban areas. Bus travel is more common in rural areas, and it might be expected that associations with physical activity would be influenced by the walkable distance between the bus stop and home, and more detailed travel data than collected in the present study would be required to explore this. Finally, it is possible that the association between travel mode and physical activity after school will reflect a greater proportion of the children who walk taking part in after-school activities or greater duration of these activities. We did not collect data on after-school activities and are unable to describe differences in participation between the travel groups.
This study showed that the transition between primary and secondary school is associated with a change in travel mode in many cases, and that this may substantially affect overall daily physical activity. These data support the development of interventions to promote active travel. As well as aiming to increase the proportion of children who actively commute overall, such interventions could focus on children who live within a walkable distance of 2.5–3 km from school and yet travel by car or bus, because these children stand to achieve substantial gains in MVPA. In addition, there is a need to explore interventions for children where overall journey distances are not appropriate for walking such as encouraging children to be dropped off at a walkable distance from school or indeed safe and pleasant cycle routes. These data also suggest that a reduction in active commuting could be a contributor to the decline in physical activity through adolescence, although further data are required to confirm this. If so, interventions to maintain active commuting throughout secondary school may be an effective approach to reducing the decline in physical activity during adolescence. Evidence to date of effective interventions is limited (4), and there is a need for more research with higher quality study designs and measures.
This work was supported by the UK National Prevention Research Initiative (G0501311) and World Cancer Research Fund (WCRF UK). This report is also a research arising from a Career Development Fellowship (to Dr. Jago) supported by the National Institute for Health Research.
We have no conflicts of interest to declare.
The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health.
The results of the present study do not constitute endorsement by American College of Sports Medicine.
1. Andersen LB, Harro M, Sardinha LB, et al.. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet
. 2006; 368: 299–304.
2. Andersen LB, Riddoch C, Kriemler S, Hills A. Physical activity and cardiovascular risk factors in children. Br J Sports Med
. 2011; 45: 871–6.
3. Badland HM, Duncan MJ, Oliver M, Duncan JS, Mavoa S. Examining commute routes: applications of GIS and GPS technology. Environ Health Prev Med
. 2010; 15: 327–30.
4. Chillon P, Evenson KR, Vaughn A, Ward DS. A systematic review of interventions for promoting active transportation to school. Int J Behav Nutr Phys Act
. 2011; 8: 10.
5. Cole T, Bellizzi M, Flegal KM. Establishing a standard definition for child overweight and obesity worldwide: an international survey. Br Med J
. 2000; 320: 1–6.
6. Cooper AR, Page AS, Foster LJ, Qahwaji D. Commuting to school. Are children who walk more physically active? Am J Prev Med
. 2003; 25: 273–6.
7. Cooper AR, Page AS, Wheeler BW, et al.. Mapping the walk to school using accelerometry combined with a Global Positioning System. Am J Prev Med
. 2010; 38: 178–83.
8. Cooper AR, Wedderkopp N, Jago R, et al.. Longitudinal associations of cycling to school with adolescent fitness. Prev Med
. 2008; 47: 324–8.
9. Cooper AR, Wedderkopp N, Wang H, Andersen LB, Froberg K, Page AS. Active travel to school and cardiovascular fitness in Danish children and adolescents. Med Sci Sports Exerc
. 2006; 38 (10): 1724–31.
10. Corder K, Ogilvie D, van Sluijs EMF. Invited commentary: physical activity over the life course—whose behaviour changes, when, and why? Am J Epidemiol
. 2009; 170: 1078–81.
11. D’Haese S, De Meester F, De Bourdeaudhuij I, Deforche B, Cardon G. Criterion distances and environmental correlates of active commuting to school in children. Int J Behav Nutr Phys Act
. 2011; 8: 88.
12. Davison KK, Werder JL, Lawson CT. Children’s active commuting to school: current knowledge and future directions. Prev Chronic Dis
. 2008; 5: 1–11.
13. Department for Transport. National Travel Survey: 2009. Travel by Age and Gender
. London: DfT publications; 2011. 7 p. Available from: http://www.dft.gov.uk/statistics/releases/national-travel-survey-2010/
14. Department for Transport/Department of Health. Active Travel Strategy
. London: DfT publications; 2010. 67 p. Available from: http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_113102
15. Department of Health. Start Active, Stay Active: A Report on Physical Activity from the Four Home Countries’ Chief Medical Officers
. London: Department of Health; 2011. 62 p. Available from: http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_128209
16. Duncan MJ, Mummery WK. GIS or GPS? A comparison of two methods for assessing route taken during active transport. Am J Prev Med
. 2007; 33: 51–3.
17. Faulkner GEJ, Buling RN, Flora PK, Fusco C. Active school transport, physical activity levels and body weight of children and youth: a systematic review. Prev Med
. 2009; 48: 3–8.
18. Garcia AW, Pender NJ, Antonakos CL, Ronis DL. Changes in physical activity beliefs and behaviors of boys and girls across the transition to junior high school. J Adolesc Health
. 1998; 22: 394–402.
19. Goodman A, Mackett RL, Paskins J. Activity compensation and activity synergy in British 8–13 year olds. Prev Med
. 2011; 53: 293–8.
20. Health and Social Care Information Centre. Health Survey for England 2008. Physical Activity and Fitness
. London, England: Health and Social Care Information Centre; 2009. 395 p. Available from: http://www.ic.nhs.uk/statistics-and-data-collections/health-and-lifestyles-related-surveys/health-survey-for-england/health-survey-for-england–2008-physical-activity-and-fitness
21. Jago R, Wedderkopp N, Kristensen PL, et al.. Six-year change in youth physical activity and effect on fasting insulin and HOMA-IR. Am J Prev Med
. 2008; 35: 554–60.
22. Janssen I, LeBlanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act
. 2010; 7: 40.
23. Lee MC, Orenstein MR, Richardson MJ. Systematic review of active commuting to school and children’s physical activity and weight. J Phys Act Health
. 2008; 5: 930–49.
24. Lubans DR, Boreham CAG, Kelly P, Foster C. The relationship between active travel to school and health-related fitness in children and adolescents: a systematic review. Int J Behav Nutr Phys Act
. 2011; 8: 5.
25. Merom D, Tudor-Locke C, Bauman A, Rissel C. Active commuting to school among NSW primary school children: implications for public health. Health Place
. 2006; 12: 678–87.
26. Nader PR, Bradley RH, Houts RM, McRitchie SL, O’Brien M. Moderate-to-vigorous physical activity from ages 9 to 15 years. JAMA
. 2008; 300: 295–305.
27. Ness AR, Leary SD, Mattocks C, et al.. Objectively measured physical activity and fat mass in a large cohort of children. PLoS Med
. 2007; 4: 476–84.
28. Pabayo R, Gauvin L, Barnett TA. Longitudinal changes in active transportation to school in Canadian youth aged 6 through 16 years. Pediatrics
. 2011; 128 (2): e404–13.
29. Panter J, Jones AP, van Sluijs EMF. Environmental determinants of active travel in youth: a review and framework for future research. Int J Behav Nutr Phys Act
. 2008; 5: 34.
30. Panter J, Jones AP, van Sluijs EMF, Griffin SJ. The influence of distance to school on the associations between active commuting and physical activity. Pediatr Exerc Sci
. 2011; 23: 72–86.
31. Petersen AC, Crockett L, Richards M, Boxer A. A self-report measure of pubertal status: reliability, validity, and initial norms. J Youth Adolesc
. 1998; 2: 117–33.
32. Riddoch CJ, Andersen LB, Wedderkopp N, et al.. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc
. 2004; 36 (1): 86–92.
33. Saksvig BI, Catellier DJ, Pfeiffer KA, et al.. Travel by walking before and after school and physical activity among adolescent girls. Arch Pediatr Adolesc Med
. 2007; 161: 153–8.
34. Strong WB, Malins RM, Blimkie CJR, et al.. Evidence based physical activity for school-age youth. J Pediatr
. 2005; 146: 732–7.
35. Timperio A, Ball K, Salmon J, et al.. Personal, family, social, and environmental correlates of active commuting to school. Am J Prev Med
. 2006; 30: 45–51.
36. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc
. 2008; 40 (1): 181–8.
37. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc
. 2011; 43 (7): 1360–8.
38. van Sluijs EMF, Fearne VA, Mattocks C, Riddoch C, Griffin SJ, Ness A. The contribution of active travel to children’s physical activity levels: cross-sectional results from the ALSPAC study. Prev Med
. 2009; 48: 519–24.