You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


1. Introduction {#sec1-ijerph-14-01334}
===============

During the last decade, physical activity levels have decreased \[[@B1-ijerph-14-01334]\] whilst sedentary-related behaviours have increased \[[@B2-ijerph-14-01334],[@B3-ijerph-14-01334]\] in the Chilean adult population. These changes have been attributable to an increase in the use of motor vehicles, which drastically influence compliance with global recommendations for physical activity \[[@B4-ijerph-14-01334]\]. However, it is not only adults who have reduced their physical activity levels. Currently, 69.1% of children and adolescents do not meet the physical activity recommendations and therefore do less than one hour of moderate to vigorous physical activity (MVPA) per day \[[@B5-ijerph-14-01334]\].

Active commuting, especially in children and adolescents, has been suggested as a feasible way of increasing their overall physical levels \[[@B6-ijerph-14-01334]\]. Walking and cycling are modes of active commuting which provide numerous health benefits \[[@B7-ijerph-14-01334],[@B8-ijerph-14-01334]\]. Public transport systems such as buses or trains also promote active commuting when compared to private transport (car commuting), as they are characterised by multimodal (active/non-active) transports \[[@B9-ijerph-14-01334]\]. Since commuting to and from school is conducted on a regular basis, the promotion of active commuting may be a feasible way of integrating physical activity into daily life and thus increasing the number of children and adolescents that meet the current physical activity guidelines \[[@B10-ijerph-14-01334]\].

Although there is increasing evidence regarding active commuting behaviours in children and adolescents, most of this evidence has been generated from developed countries, such as Canada, U.S., Australia and European countries \[[@B10-ijerph-14-01334],[@B11-ijerph-14-01334],[@B12-ijerph-14-01334],[@B13-ijerph-14-01334],[@B14-ijerph-14-01334],[@B15-ijerph-14-01334],[@B16-ijerph-14-01334]\]. However, there is a lack of evidence from developing countries, including Chile. Considering the rapid economic growth and fast transition towards a westernised lifestyle that Chile has experienced during the last three decades \[[@B6-ijerph-14-01334]\], it is essential to characterise and quantify the trend and patterns of active commuting to and from the school in children and adolescents. This information could inform public health authorities for the implementation and promotion of active commuting as a vehicle for tackling childhood obesity but also non-communicable diseases associated with a lack of physical activity \[[@B17-ijerph-14-01334]\]. The aim of this study was, therefore, to characterise patterns of active commuting to and from schools in children and adolescents in Chile.

2. Material and Methods {#sec2-ijerph-14-01334}
=======================

2.1. Study Design and Participants {#sec2dot1-ijerph-14-01334}
----------------------------------

This cross-sectional study was conducted in the urban city of Valparaiso in Chile. The sample is non-probabilistic and intentional. A total of 421 students (168 children and 253 adolescents) aged between 10 and 18 years old (equivalent to children in 5th and 7th grade and adolescents attending grades 8 to 12) were included in this study. These participants belonged to three public and two private schools from the Valparaíso region.

2.2. Instruments {#sec2dot2-ijerph-14-01334}
----------------

The mode of commuting to and from school was measured using a questionnaire for children and adolescents developed by the University of Granada (Spain) through the project "PACO: pedalea y anda al colegio" (<http://profith.ugr.es/paco>). This questionnaire has been validated in Spanish population \[[@B18-ijerph-14-01334]\] and was the result of a systematic review of 158 studies in the scientific literature around the assessment of the mode of commuting using questionnaires \[[@B19-ijerph-14-01334]\]. Questions were selected based on their appropriateness towards the objective of the study. Students completed this questionnaire with assistance from their teacher. The questionaire included closed-ended questions with multiple answers. The usual mode of commuting to and from school was categorised into "active" commuting (walking and cycling) and non-active commuting (car, motorcycle, or bus). For commuting distance from home to their school participants could choose any of the following options: 0 to 0.5 km, 0.6 to 1 km, 1.1 to 2 km, 2.1 to 3 km, 3.1 to 5 km and \>5 km. The reliability of the questionaire was measured in a sub-sample of 219 Chilean children and adolescents by applying the same questionnaire on two different occasions 7 days apart. The usual mode to and from school shows a high reliability (Kappa \> 0.85), whereas commuting distance shows a moderate reliability (Kappa \> 0.69).

2.3. Procedure {#sec2dot3-ijerph-14-01334}
--------------

The questionnaire was completed between April and June 2017. The students completed the questionnaires in 15 to 30 min with the assistance of their school teachers.

2.4. Ethical Aspects {#sec2dot4-ijerph-14-01334}
--------------------

Prior to data collection, parents and students were informed about the characteristics of the questionnaire, the study purpose, and the confidentiality of the results. All questionnaires were answered voluntarily and with parental consent. Therefore, all participants gave informed consent for participating in the study, following the rules, with the study approved by Ethics Committee of the Pontificia Universidad Católica de Valparaíso (code: CCF02052017) and the 2004 Helsinky statement.

2.5. Data Analysis {#sec2dot5-ijerph-14-01334}
------------------

The results are presented as means and standard deviations for continuous variables and as a prevalence rate for categorical variables. For analysis purposes, the sample was stratified into groups by sex and age for both children and adolescents. Differences in commuting mode between gender (boys vs. girls), age (children vs. adolescents) and commuting route (to school vs. from school) were assessed using the Chi-square test. Differences for commuting distance by age and commuting mode were also investigated using Chi-square test. All statistical analyses were performed using the statistical program SPSS^®^ version 21 (IBM, New York, NY, USA). A *p*-value (\<0.05) was set as significant in all analyses.

3. Results {#sec3-ijerph-14-01334}
==========

[Table 1](#ijerph-14-01334-t001){ref-type="table"} presents the characteristics of the school children participating in the study including gender, age and type of school (Private or Public).

The modes of commuting to and from school for both children and adolescents are shown in [Table 2](#ijerph-14-01334-t002){ref-type="table"}. The proportion of children and adolescents who reported walking to and from the school was 11% and 24.8%, respectively. In children, car commuting was the main commuting mode reported (64.9% going to school and 51.2% returning from school), whereas for adolescents 50.2% reported commuting to school by car but 37.5% reported using the bus to return from school.

The modes of commuting between children and adolescents were statistically different, except for the metro or train commuting. Adolescents reported walking more frequently than children (*p* \< 0.001). They also reported a lower use of car commuting and a higher use of public transport when compared to children. Interestingly, children commuted more by motorcycle (in the company of their parents) than adolescents (*p* \< 0.001), while neither children nor adolescents used bicycles as a prominent mode of commuting to school.

Differences between the modes of commuting by route of commuting (to and from school) are shown in [Figure 1](#ijerph-14-01334-f001){ref-type="fig"}. Car commuting was the main commuting mode used for both children and adolescents to travel from home to the school. However, car, public bus and walking were the main commuting modes used to return from school in adolescents. Car commuting remains the main commuting mode for returning from school in children.

The commuting distances from home to school in children and adolescents are shown in [Table 3](#ijerph-14-01334-t003){ref-type="table"}. Commuting distances ranging from 0 to 5 km did not show any significant differences between children and adolescents. However, commuting distances greater than 5 km are significantly higher for adolescents than for children.

[Figure 2](#ijerph-14-01334-f002){ref-type="fig"} shows the percentage of children by commuting distance and commuting mode. The results show that active commuters are more likely to commute shorter distances (\<1 km), whereas the proportion of non-active commuters increases for middle and long distances (\>1 km) in both children and adolescents.

4. Discussion {#sec4-ijerph-14-01334}
=============

The main findings of this study indicate that car commuting is the most popular commuting mode in both children (to school 64.9%; from school 51.2%) and adolescents (to school 50.2%; from school 24.7%). Most adolescents, however, used the public bus to return from school. Interestingly, walking was the only active mode used, with no participants reporting cycling to or from school. The proportion of walking commuters is higher in adolescents than in children. As expected, the percentage of both children and adolescents who walk to school was higher for those who commute short distances. It is worthy of note that this is the first study addressing patterns of different commuting modes in young Chilean students, which reinforces the need for more research on this subject.

4.1. Patterns of Mode of Commuting {#sec4dot1-ijerph-14-01334}
----------------------------------

Less than 10% of children and around 25% of adolescents reported commuting by walking in the present study. These results are lower than those reported by other developed countries such as Spain where 62.4% of schoolchildren walked or cycled to school \[[@B17-ijerph-14-01334]\], or Belgium where 65.2% of girls and 65.5% of boys used active commuting to school \[[@B16-ijerph-14-01334]\]. However, adolescents from North America showed a similar proportion of active commuting to those observed in our study \[[@B20-ijerph-14-01334],[@B21-ijerph-14-01334]\], 15% of 13-year-old Canadian students walked to school \[[@B19-ijerph-14-01334]\], and only 8% of adolescents between the ages of 14 and 17 in the USA walked to school once a week \[[@B21-ijerph-14-01334]\].

Interestingly, none of the participants included in this study reported cycling to or from school. Similarly, in Spain, only 0.5% of students cycle to school \[[@B17-ijerph-14-01334]\]. However, in Belgium, 44.4% of students used a bicycle as a means of transport \[[@B16-ijerph-14-01334]\]. In the Netherlands, 31.8% of school children cycle to school \[[@B22-ijerph-14-01334]\]. Cultural and road safety differences within these countries could explain the differences observed within Chile, Belgium and The Netherlands. These countries have a large tradition of cycling, not only in students, but in the whole population \[[@B14-ijerph-14-01334],[@B22-ijerph-14-01334]\]. In addition, it is important to bear in mind the geographical context of the region of Valparaíso, where the Chilean participants came from. Valparaíso has important inconveniences such as long distances from home to school and the different geography between the coast and the hills inland. Moreover, the scarce availability of bicycle paths (only 2 km) does not favour the use of this mode of transport. Other cities in Chile, such as Rancagua, Curicó, and Talca, show a higher use of bicycles in daily commuting (4%, 12% and 8%, respectively) \[[@B23-ijerph-14-01334]\]. These cities are located on flat landscapes and are characterised by being geographically more accessible.

Our results show that car commuting is the most popular mode of commuting for both children and adolescents. However, adolescents reported using public bus as the second most common mode of commuting from school to home. These data show the increasing level of independent mobility that adolescents have when compared to children. In addition, the use of public transport also implies some form of active commuting \[[@B24-ijerph-14-01334],[@B25-ijerph-14-01334]\], and it is more highly recommended for young people than private transport from a health and autonomous point of view. A study conducted in the US showed that children undertake an average of 19 min physical activity to walk from home or school to the public transport stops or stations \[[@B8-ijerph-14-01334]\].

4.2. Distance from Home to School {#sec4dot2-ijerph-14-01334}
---------------------------------

This study revealed that more than 35% of children and adolescents live more than 5 km away from school and only 20% of children live less than 0.5 km away. These findings may suggest that long distances between home and school is one of the main limitations in the promotion of active commuting behaviours in children and adolescents. An Australian study \[[@B26-ijerph-14-01334]\] reported that the greater the distance between home and school, the lower the percentage of school children who actively commute every day. In addition, this study pointed out that school children who live less than 750 m away from school are more active. This data coincides with the adolescent group in the present study. It has been established that school children living less than 800 m from school, are 5 times more likely to walk \[[@B27-ijerph-14-01334]\]. A Spanish study identified 800 m as a walkable distance for children going to school \[[@B28-ijerph-14-01334]\]. In addition, other researchers established differences with cut off points at 875 m in children and 1.35 km in adolescents \[[@B29-ijerph-14-01334]\]. This means that the threshold distance for active commuting increases with age. For example, Chillón et al. \[[@B30-ijerph-14-01334]\], established a threshold distance among English young people at 1.4, 1.6 and 3.0 km for students aged 10, 11 and 14 years old, respectively.

Given that schools are far from residential areas, developing catchment areas policies to reduce distances and promote active commuting could bring the Chilean context closer to that of other countries in Europe. These results provide several insights that should be further analysed and improved on within the Chilean society. The main issue is that parents in Chile travel more than an hour on a daily basis to take their children to private schools---which are considered to be more qualified than the public schools located closer to family homes. This trend negatively affects the promotion of active commuting to school, since longer distances often force families to use their cars. It would be helpful to promote active commuting by improving governmental policies that ensure safety and reduce barriers to active commuting. Furthermore, it may help society to tackle the high inactivity and alarming childhood and adolescent obesity rate in Chile.

4.3. Strengths and Limitations {#sec4dot3-ijerph-14-01334}
------------------------------

The main strengths of this study are that this is the first study to describe the modes of commuting in Chilean children and adolescents, and that it provides reliable data originating from the questionnaire. Limitations are the low number of total participants, the low heterogeneity of the Chilean cities included in the sample, and the self-reported distance from home to school.

5. Conclusions {#sec5-ijerph-14-01334}
==============

The most common mode of commuting in children and adolescents from Valparaiso is the car, and the rate of walking was lower than in other European and North American countries. This study shows higher independent mobility in adolescents (walking and public transportation) when compared to children. In addition, the long distance from home to school appears to be a main barrier in the promotion of active modes of commuting among the Chilean school children. These results provide unique information on active commuting patterns in school children which could be used to inform public health authorities for the promotion and facilitation of active commuting in children and adolescents. This, in turn, could have important implications for health during adulthood.

To the teachers and students of the participating schools, the CONICYT PAI-MEC programme (MEC 80150030), the Academic Incentive Programs from the Research Department of the Pontificia Universidad Católica de Valparaíso, Valparaiso-Chile and funding from the University of Granada 2016 (Unit of Excellence on Exercise and Health---UCEES). We are grateful to Carmen Sainz-Quinn for assistance with the English language.

Study conception and design: Fernando Rodríguez-Rodríguez; Palma Chillon. Acquisition of data: Danica Escobar-Gómez; Fernando Rodríguez-Rodríguez. Analysis and interpretation of data: Fernando Rodríguez-Rodríguez; Danica Escobar-Gómez; Palma Chillon. Drafting of manuscript: Fernando Rodríguez-Rodríguez; Carlos Cristi-Montero; Carlos Celis-Morales; Danica Escobar-Gómez; Palma Chillon. Critical revision: Fernando Rodríguez-Rodríguez; Carlos Cristi-Montero; Carlos Celis-Morales; Danica Escobar-Gómez; Palma Chillón.

The authors declare no conflict of interest.

![Comparison of the modes of commuting to and from school, in children (**A**) and adolescents (**B**). \* *p* \< 0.05 value between "To school" and "From school" in children and adolescents by car and walking.](ijerph-14-01334-g001){#ijerph-14-01334-f001}

![Distances from home to school according to active or non-active modes of commuting in children (**A**) and adolescents (**B**).](ijerph-14-01334-g002){#ijerph-14-01334-f002}

ijerph-14-01334-t001_Table 1

###### 

Descriptive sociodemographic data of the participants.

  Characteristics   Overall   Children   Adolescents                   
  ----------------- --------- ---------- ------------- -------- ------ --------
  All               453       \(100\)    171           (37.7)   282    (60.6)
  Gender                                                               
  Male              228       (50.3)     86            (50.3)   142    (50.4)
  Female            225       (49.7)     85            (49.7)   140    (49.6)
  Type of school                                                       
  Private School    258       (57.0)     85            (49.7)   173    (61.3)
  Public School     195       (43.0)     91            (53.2)   104    (36.9)
  Age (mean + SD)   12.7      ±2.0       10.6          ±0.6     13,9   ±1.5

SD: Standard deviation.

ijerph-14-01334-t002_Table 2

###### 

Mode of commuting to and from school by sex in children and adolescents.

  ---------------------- -------------- ----------------- ------------- ------------- ------------- ------------- ---------------
                         **Children**   **Adolescents**                                                           
  **To School**          **Overall**    **Boys**          **Girls**     **Overall**   **Boys**      **Girls**     
                         ***n* (%)**    ***n* (%)**       ***n* (%)**   ***n* (%)**   ***n* (%)**   ***n* (%)**   ***p*-Value**
  Active commuting                                                                                                
  Walking                15 (8.9)       9 (10.7)          6 (7.1)       59 (23.3)     31 (25.4)     28 (21.4)     \<0.001
  Bicycle                0 (0.0)        0 (0.0)           0 (0.0)       0 (0.0)       0 (0.0)       0 (0.0)       \-
  Non-active commuting                                                                                            
  Car                    109 (64.9)     54 (64.3)         55 (65.5)     127 (50.2)    54 (44.3)     73 (55.7)     0.003
  School Bus             12 (7.1)       8 (9.5)           4 (4.8)       6 (2.4)       4 (3.3)       2 (1.5)       \<0.001
  Public Bus             4 (2.4)        3 (3.6)           1 (1.2)       57 (22.5)     33 (27.0)     24 (18.3)     0.018
  Metro/Train            3 (1.8)        2 (2.4)           1.2 (1)       2 (0.8)       0 (0.0)       2 (1.5)       0.356
  Motorcycle             25 (14.9)      8 (9.5)           17 (20.2)     2 (0.8)       0 (0.0)       2 (1.5)       \<0.001
                         **Children**   **Adolescents**                                                           
  **From School**        **Overall**    **Boys**          **Girls**     **Overall**   **Boys**      **Girls**     
                         ***n* (%)**    ***n* (%)**       ***n* (%)**   ***n* (%)**   ***n* (%)**   ***n* (%)**   ***p*-Value**
  Active commuting                                                                                                
  Walking                23 (13.1)      14 (15.7)         9 (10.7)      81 (32.2)     44 (36.4)     37 (28.5)     \<0.001
  Bicycle                0 (0.0)        0 (0.0)           0 (0.0)       0 (0.0)       0 (0.0)       0 (0.0)       \-
  Non-active commuting                                                                                            
  Car                    86 (51.2)      45 (53.6)         41 (48.8)     62 (24.7)     28 (23.1)     34 (26.2)     \<0.001
  School Bus             25 (14.9)      9 (10.7)          16 (19.0)     8 (3.2)       1 (0.8)       7 (5.4)       \<0.001
  Public Bus             6 (3.6)        4 (4.8)           2 (2.4)       94 (37.5)     48 (39.7)     46 (35.4)     \<0.001
  Metro/Train            2 (1.2)        2 (2.4)           0 (0.0)       4 (1.6)       0 (0.0)       4 (3.1)       0.734
  Motorcycle             26 (15.5)      10 (11.9)         16 (19.0)     1 (0.4)       0 (0.0)       1 (0.8)       \<0.001
  ---------------------- -------------- ----------------- ------------- ------------- ------------- ------------- ---------------

Chi square test (*p* \< 0.05) was used to assess diferecnes in commuting modes by children and adolescents. Significant differences were set at *p* \< 0.05.

ijerph-14-01334-t003_Table 3

###### 

Distances from home to school in children and adolescents.

  Distance    Children   Adolescents                    
  ----------- ---------- ------------- ------- -------- -------
  0--0.5 km   13.1%      \(22\)        16.1%   \(41\)   0.711
  0.6--1 km   13.7%      \(23\)        14.5%   \(37\)   0.056
  1.1--2 km   14.3%      \(24\)        13.3%   \(34\)   0.078
  2.1--3 km   9.5%       \(16\)        8.2%    \(21\)   0.211
  3.1--5 km   13.1%      \(22\)        10.6%   \(27\)   0.621
  \>5 km      36.3%      \(61\)        37.3%   \(95\)   0.039

Data presented as % (number of individuals). Significant differences were accepted at *p* \< 0.05.