Objective Measurement of the Mode of Commuting to School Using GPS: A Pilot Study

Abstract

Background and objectives: Active commuting to school (ACS) is a promising strategy to increase the daily physical activity (PA) in youths. However, more studies are required to objectively quantify the mode of commuting to school, as well as the health impact of this behavior. Thus, the aims of this study were: (1) to objectively determine the mode of commuting to school using GPS; (2) to quantify the sedentary time, PA levels, energy expenditure, and the steps derived from each mode of commuting; and (3) to analyze the associations between ACS trips and sedentary time, PA, energy expenditure, and steps. Participants and Methods: A total of 180 trips to school were detected, which corresponded to 18 adolescents (12 girls, mean age = 15 ± 0.0 years old). Mode of commuting to school was detected using a novel method merging GPS data in the Personal Activity Location Measurement System (PALMS), whereas sedentary time, PA levels, energy expenditure, and steps were objectively evaluated through accelerometry. Logistic regressions were used to analyze the associations of these variables with walking trips. Results: A total of 115 trips were recorded. Most trips were performed by walk (49.5%), followed by vehicle (39.1%) and mixed transport (11.3%). In the active school trips, youths were less likely to spend minutes in sedentary behaviors (OR: 0.481, p = 0.038), a higher increase on Metabolic-Equivalent of Task (METs) (OR: 5.497, p = 0.013), and greater steps (OR: 1.004, p = 0.029) than in the passive school trips (both active and passive modes were objectively measured). Conclusions: ACS (mainly walking) contribute to higher METs and steps in adolescents. GPS could be an appropriate method to objectively evaluate the PA variables related to the ACS trips.

Mediation role of residential density on the association between perceived environmental factors and active commuting to school in Brazilian adolescents

“…Finally, larger buffers size was not used and adolescents' ACS time was not determined. Future proposals should include more objective measurements of this behavior 41 . The strengths of the present study include a large sample of Brazilian adolescents and the use of environmental variables measured objectively and subjectively to understand how much this reflects in ACS.…”

Objective Measures to Assess Active Commuting Physical Activity to School in Young People: A Systematic Review Protocol and Practical Considerations

“…A previous systematic review with meta-analysis, conducted more than 4 years ago, showed that the contribution of walking to school to objectively MVPA on school days ranges from 13 min/day to 17 min/day in secondary-school students and primary-school student, respectively [ 5 ]. Given the increase of ACS studies among children and adolescents in the last years [ 18 , 19 , 20 , 24 , 25 , 26 , 29 , 30 , 31 , 32 , 33 ] it will be also important to carry out an update of this systematic review including not only walking to/from school but also cycling to/from school. Moreover, since walking is usually a light-intensity activity and provides numerous health benefits [ 1 ] it seems interesting to know the contribution of walking and cycling to/from school to light PA levels.…”

“…For instance, several authors such as Pizarro et al [ 24 ] and Villa-Gonzalez et al [ 25 ] assessed the commuting-related PA to/from school through both accelerometer and Global Positioning System (GPS). Several authors have suggested that the combination of data from these two objective measures (i.e., accelerometers and GPS) might allow evaluating cycling to school properly [ 24 , 25 ]. Tarp et al [ 20 ] combined GPS, accelerometers, and heart rate to assess commuting-related PA among adolescent who cycled to school.…”

“…Therefore, light PA levels, cycling PA to/from school, and other objective measures were not taken into account [ 5 ]. In addition, given in the last year the number of studies about ACS has increased [ 18 , 19 , 20 , 24 , 25 , 26 , 29 , 30 , 31 , 32 , 33 ] and the literature search in the aforementioned review was conducted over 4 years ago, it seems necessary to update this systematic review [ 5 ]. Furthermore, only one systematic review has identified the different data collection protocols and data processing criteria of the studies that analysed PA, sedentary time, sleep time, and PA energy expenditure (PAEE) through ActiGraph GT3X, in all population (i.e., preschoolers, children, adolescents, adults, and older adults) [ 34 ].…”

Contribution of active commuting to and from school to device‐measured physical activity levels in young people: A systematic review and meta‐analysis

“…49 A total of 42.9% (n = 6) of the included studies were conducted in children (n = 3525) [36][37][38][39]44,49 and 57.1% (n = 8) in adolescents (n = 3602). [40][41][42][43][45][46][47][48] Regarding trip direction, eight of the included studies only assessed the home-school trip, [36][37][38][39][42][43][44][45] and six of the included studies assessed both trips direction (i.e., home-school and school-home) 40,41,[46][47][48][49] (see Table 1 for more details). The device used to measure PA in all included studies was the accelerometer.…”