Journal of Physical Activity and Health, 2015, 12, 1088 -1095 http://dx.doi.org/10.1123/jpah.2014-0103 © 2015 Human Kinetics, Inc.
ORIGINAL RESEARCH
Active Commuting to School in Mexican Adolescents: Evidence From the Mexican National Nutrition and Health Survey Alejandra Jáuregui, Catalina Medina, Deborah Salvo, Simon Barquera, and Juan A. Rivera-Dommarco Background: Travel to school offers a convenient way to increase physical activity (PA) levels in youth. We examined the prevalence and correlates of active commuting to school (ACS) in a nationally representative sample of Mexican adolescents. A secondary objective was to explore the association between ACS and BMI status. Methods: Using data of adolescents (10–14 years old) from the 2012 Mexican National Health and Nutrition Survey (n = 2952) we ran multivariate regression models to explore the correlates of ACS and to test the association between ACS and BMI z-score or overweight/obesity. Models were adjusted for potential confounders and design effect. Results: 70.8% of adolescents engaged in ACS (walking: 68.8%, bicycling: 2.0%). ACS was negatively associated with travel time, age, mother’s education level, household motor vehicle ownership, family socioeconomic status, and living in urban areas or the North region of the country (P < .05). Time in ACS was negatively associated with overweight/obesity: Each additional minute of ACS was associated with a 1% decrease in the odds for being overweight or obese (P < .05). Conclusions: Potential correlates of ACS that may result in benefits for Mexican adolescents are identified. More studies on this relationship are needed to develop interventions aimed at increasing PA through ACS in Mexico. Keywords: active transport, physical activity, built environment, obesity, pediatrics
Physical inactivity is a pandemic affecting all age groups,1,2 and is a risk factor for obesity and chronic diseases, including type II diabetes.3 In Mexico, childhood obesity is a public health priority, given that almost 35% of children and adolescents are either overweight or obese.4 Furthermore, type II diabetes has become more prevalent among Mexican children and adolescents in recent years4,5 and is the primary cause of death in the general population.6 Increasing physical activity (PA) among youth is essential for obesity prevention.7 Strategies to promote PA in this age group have emphasized increases in leisure-time and school-based activities. However, in Mexico leisure-time activities have become more sedentary.4 Furthermore, the current potential for school interventions to increase PA is limited by the fact that in public schools, children spend only 4.5 to 5 hours in school, with most of this time allocated to classroom work.8 More recently, walking or bicycling to school, or active commuting to school (ACS), has been identified as a potential source to increase PA in daily routines.9 Reports from high-income countries (HIC) show that youth who actively commute to school are more likely to meet PA recommendations than those who travel to school by car or bus.10,11 Nevertheless, the evidence linking ACS with body composition and fitness remains inconclusive.11 Important positive correlates of ACS in HIC are male gender,12 low family socioeconomic status,12,13 no car ownership in the family,14 short distance to school,12 no crime and traffic concerns,15,16 or no major roads crossings on the way to school.15,17 The authors are with the Instituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico. Medina is also with the School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada. Salvo is also with the Michael & Susan Dell Center for Healthy Living, at the University of Texas Health Science Center at Houston, School of Public Health (Austin Regional Campus). Jáuregui ([email protected]) is corresponding author. 1088
Research on ACS is scarce in Latin America and in low-tomiddle-income countries (LMIC) in general.18–21 Two studies reported negative associations between ACS and overweight22 or adiposity.23 Some studies have identified distance to school20,21 and crime and traffic as correlates of ACS in Latin America.21 In Mexico, ACS among youth has not been studied yet. It is important to identify the correlates of ACS in Mexico, as these may vary from those known for HIC, due to socioeconomic, political and structural differences, as has been reported for other Latin American countries.24,25 Addressing this gap in knowledge is important for future surveillance and monitoring of ACS in Mexico, and to design intervention programs using ACS to increase daily PA. The objective of our study was to analyze the prevalence and correlates of ACS in a nationally representative sample of Mexican adolescents aged 10 to 14 years. A secondary objective was to explore the association between ACS and BMI status.
Methods Design and Participants We used data from the 2012 Mexican National Health and Nutrition Survey (ENSANUT 2012). ENSANUT 2012 used a cross-sectional study design, with a probabilistic multistage stratified cluster sample of Mexican households. It is representative of the 4 regions of the country (North, Central, South and the metropolitan area of Mexico City) and of urban (≥ 2 500 residents) and rural (< 2500 residents) localities. Data were collected between October 2011 and May 2012. A total of 3399 adolescents 10 to 14 years old were interviewed, representing 11,339,401 individuals in this age range. A detailed description of the methodology is published elsewhere.26 All participants provided informed consent before participating. The Ethics Review Board of the National Institute of Public Health of Mexico approved the survey.
Active Commuting to School in Mexican Adolescents 1089
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Commuting Modes and Travel Times Data on commuting mode to school and travel time (from home to school and back), a proxy for travel distance, were collected from adolescents 10 to 14 years old by a computer-assisted interview. Adolescents received help from their parents/guardians to answer the interview. To assess ACS, relevant survey items from the Health Behavior in School-Aged Children Survey (HBSC) were culturally adapted and pilot tested for in-person administration, during a 2-week period, in a Mexican setting. The HBSC is a reliable tool (Cronbach’s alpha ≥ 0.8) that includes specific items to assess ACS among school-aged children and adolescents.27 Based on this, participants were asked: ‘On a typical day, the longest part of your journey from home to school and from school to home is made by. . . ?’Response choices were 1) walking; 2) bicycle ridden by me; 3) bicycle ridden by somebody else (this option was added to the Mexican HBSC as a result of the findings of the pilot testing phase); 4) bus, metro, or train; 5) car or motorcycle; and 6) other. Per the HBSC scoring protocol,27 ACS was defined as those individuals choosing options 1 and 2, but not 3–6. Travel time was assessed by the following question: In a typical week, how much time do you spend commuting to school? Answers were reported in hours and minutes.
Demographic Variables Demographic information was obtained from the parent/guardian and their child using a questionnaire. Information on mother’s education level and working status were collected. The questionnaire also included items on sociodemographic characteristics of the head of the household, family structure, household characteristics and assets (including car ownership), household consumption patterns (expenditure and rent), and neighborhood poverty. With these, plus additional information on the neighborhood poverty index (from the National Institute of Geography and Statistics), the ENSANUT 2012 generated a household socioeconomic status indicator using a predictive model of income (decile-based), based on the National Income and Expenditure Survey 2010.28 This indicator was found to adequately describe the heterogeneity of standard socioeconomic variables, such as schooling years of the head of the household, income, access to services, and household assets.28
Screen Time and Participation in Organized Physical Activities Screen time and participation in organized activities or sports during the past 12 months were ascertained by questionnaire. Participants responded to the following questions. 1. ‘During the past 12 months, in how many sport teams, individual sports or organized physical activities did you take part in a competitive fashion, or very frequently, such as extracurricular activities or club/varsity teams?’ Response options were 1) none, 2) 1, 3) 2, 4) 3, and 5) 4 or more. 2. ‘In a weekday (Monday-Friday), how many hours do you spend in front of a screen, either watching television, movies, soap operas, or playing videogames?’ Response options were 1) none, 2) 1 to 2 hours, 3) 3 to 4 hours, 4) 5 to 6 hours, 5) 7 to 8 hours, and 6) 9 or more hours. The same question was used to collect weekend screen-time.
BMI Status Weight and height were objectively measured by trained field workers following standardized procedures.29 BMI z-scores (BMI =
kg/m2) were calculated and classified as overweight (between +1 and +2 SD) and as obese (above +2 SD) according to the WHO growth references.30,31
Statistical Analysis Descriptive statistics using frequencies and means (SD) were obtained. Proportions of travel modes and ACS were calculated. Dummy variables for ACS (0 = No, 1 = Yes) and weight status (0 = Normal, 1 = Overweight or obese) were generated. Correlates of ACS were analyzed using bivariate and multivariate logistic regression models. Independent variables included travel time, demographic variables (sex and age), sedentary and PA behavior (taking part in organized activities or sports and screen time), family characteristics (family socioeconomic status and motor vehicle ownership, mother’s education level and working status), and geographic variables (locality and country region). To determine if ACS was associated with BMI, we conducted a series of regression analyses with ACS as the independent variable and z-scored BMI or BMI category (normal vs. overweight or obesity) as the dependent variable. Since it has been suggested that more time spent in ACS may be inversely associated with BMI,32–34 we included an interaction term between commuting mode and travel time to test this relationship. The model was adjusted for the effect of potential confounders (same covariates as previous model). All adjusted models were tested for specification error, goodness of fit and multicollinearity. Differences between unadjusted means and proportions and regression model estimates were considered significant if P < .05 for main effects and P < .1 for interactions.35 All analyses were performed using the survey weights commands in STATA 11.0 (StataCorp, College Station, Texas, USA) (eg, svy) to take into account the complex survey design.
Results Of the 3399 adolescents measured, 94 did not attend school and 354 were excluded for missing data, leaving a sample size of 2952 adolescents in the analysis of ACS correlates, representing 11,417,260 adolescents. No differences were found between the excluded adolescents and the analytical sample. Among the analytical sample, the mean age was 12.0 (±0.9) years old, a total of 52.1% were male, and 51.3% were attending elementary school. Almost 40% of the families owned a motorized vehicle, 62.4% of the mothers reported not working, and 77.7% had an education level lower than high school (Table 1), which is consistent with data from the 2010 Mexican census.
Prevalence of Active Commuting to School A total of 70.8% (walking: 68.8%, bicycling: 2.0%) of the adolescents engaged in ACS, while 14.6% used public transportation, 13.3% were driven to school in a private motorized vehicle, and 1.3% used a combination of travel modes or were ridden to school by somebody else (Table 2). Travel time to school was of 20.2 min (Inactive commuting = 26.3, 95% CI: 24.1–28.8; ACS = 18.1, 95% CI: 17.1–19.2). ACS was more prevalent among male adolescents (74.1%), adolescents from mothers with no education (94.2%), from the lowest socioeconomic status (86.6%), and from families without motorized vehicle ownership (78.6%), in rural areas (82.7%), and in the South region of the country (82.3%) (Table 3).
JPAH Vol. 12, No. 8, 2015
Table 1 Frequency Distributions for Demographic Variables for Mexican Adolescents in 2011–2012; Mexican National Nutrition and Health Survey, 2012 Variable Age (years)b
n
%a
95% CIa
2952
12
11.9–12.0
Sex Male
1528
52.1
49.1–55.2
Female
1424
47.9
44.8–50.9
Participation in structured activities None
1690
56.9
53.7–60.1
1 or 2
1184
40.8
37.7–43.8
78
2.3
1.5–3.1
3 or more
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Screen time Less or equal to 2 hr/d
1034
32.8
30.1–35.6
More than 2 and less than 4 hr/d
1146
39.7
36.8–42.7
More than 4 hr/d
772
27.4
24.7–30.1
Elementary school
1589
51.3
48.3–54.3
Secondary school
1354
48.7
45.7–51.7
1055
35.6
32.6–38.6
School level
Family SES quintile Quintile 1 Quintile 2
685
24
21.5–26.6
Quintile 3
516
16.9
14.6–19.2
Quintile 4
478
15.6
13.7–17.5
Quintile 5
218
7.9
6.3–9.5
No
1973
62.4
59.4–65.5
Yes
979
37.6
34.5–40.6
None
129
5.1
3.8–6.5
Less than high school
2209
72.6
69.8–75.4
High school or more
614
22.3
19.8–24.8
Mother’s working status
Mother’s education level
Motor vehicle in the family No
1726
61
57.9–64.1
1
1024
31.9
29.1–34.6
More than 1
202
7.1
5.6–8.7
Locality Rural (≤ 2500 inhab)
1056
30.3
28.0–32.6
Urban (> 2500 inhab)
1896
69.7
67.4–72.0
Region of the country North
813
18.6
17.2–20.1
Central
1003
32.1
29.8–34.5
South
991
33.8
31.4–36.1
Metropolitan area
145
15.4
13.2–17.6
1782
62.1
59.1–65.0
BMI classification Normal
a b
1090
Overweight
618
21.7
19.1–24.3
Obese
513
16.2
14.0–18.5
Weighted for probability of selection. Frequency, mean, and 95% CI. JPAH Vol. 12, No. 8, 2015
Table 2 Proportion and Median Travel Time by Commuting Mode to School in Mexican Adolescents in 2011–2012; Mexican National Nutrition and Health Survey, 2012 Prevalence n
%a
Transportation mode Walking
2054
68.9
Bicycling
73
Bicycle ridden by somebody else
16
Public transportation Private motorized vehicle Combination of travel modesc
95%
Travel time (min)
CIa
Meana,b
95% CIa,b
66.0–71.8
18.2
17.2–19.3
2.0
1.2–2.8
15.1
9.1–25.0
0.7
0.0–1.5
19.0
16.6–21.9
370
14.6
12.2–17.0
34.1
29.7–39.2
423
13.3
11.4–15.2
20.0
18.0–22.2
15
0.6
0.0–1.1
29.2
24.0–35.5
Weighted for probability of selection. Means and 95% CI derived from log-transformed variable. c Defined as combining an active transportation mode (walking or bicycling) on the way to school plus an inactive transportation mode (private motorized vehicle) on the way back home, or vice versa. a
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b
Table 3 Weighted Prevalence and Correlates of Active Commuting to School (Walking or Biking) Among Mexican Adolescents in 2011–2012; Mexican National Nutrition and Health Survey, 2012 Unadjusted prevalence/mean
Bivariate
Adjustedb
% or Mean
95% CI
OR
P
OR
P
20.2
18.8–21.7
0.98
ORIGINAL RESEARCH
Active Commuting to School in Mexican Adolescents: Evidence From the Mexican National Nutrition and Health Survey Alejandra Jáuregui, Catalina Medina, Deborah Salvo, Simon Barquera, and Juan A. Rivera-Dommarco Background: Travel to school offers a convenient way to increase physical activity (PA) levels in youth. We examined the prevalence and correlates of active commuting to school (ACS) in a nationally representative sample of Mexican adolescents. A secondary objective was to explore the association between ACS and BMI status. Methods: Using data of adolescents (10–14 years old) from the 2012 Mexican National Health and Nutrition Survey (n = 2952) we ran multivariate regression models to explore the correlates of ACS and to test the association between ACS and BMI z-score or overweight/obesity. Models were adjusted for potential confounders and design effect. Results: 70.8% of adolescents engaged in ACS (walking: 68.8%, bicycling: 2.0%). ACS was negatively associated with travel time, age, mother’s education level, household motor vehicle ownership, family socioeconomic status, and living in urban areas or the North region of the country (P < .05). Time in ACS was negatively associated with overweight/obesity: Each additional minute of ACS was associated with a 1% decrease in the odds for being overweight or obese (P < .05). Conclusions: Potential correlates of ACS that may result in benefits for Mexican adolescents are identified. More studies on this relationship are needed to develop interventions aimed at increasing PA through ACS in Mexico. Keywords: active transport, physical activity, built environment, obesity, pediatrics
Physical inactivity is a pandemic affecting all age groups,1,2 and is a risk factor for obesity and chronic diseases, including type II diabetes.3 In Mexico, childhood obesity is a public health priority, given that almost 35% of children and adolescents are either overweight or obese.4 Furthermore, type II diabetes has become more prevalent among Mexican children and adolescents in recent years4,5 and is the primary cause of death in the general population.6 Increasing physical activity (PA) among youth is essential for obesity prevention.7 Strategies to promote PA in this age group have emphasized increases in leisure-time and school-based activities. However, in Mexico leisure-time activities have become more sedentary.4 Furthermore, the current potential for school interventions to increase PA is limited by the fact that in public schools, children spend only 4.5 to 5 hours in school, with most of this time allocated to classroom work.8 More recently, walking or bicycling to school, or active commuting to school (ACS), has been identified as a potential source to increase PA in daily routines.9 Reports from high-income countries (HIC) show that youth who actively commute to school are more likely to meet PA recommendations than those who travel to school by car or bus.10,11 Nevertheless, the evidence linking ACS with body composition and fitness remains inconclusive.11 Important positive correlates of ACS in HIC are male gender,12 low family socioeconomic status,12,13 no car ownership in the family,14 short distance to school,12 no crime and traffic concerns,15,16 or no major roads crossings on the way to school.15,17 The authors are with the Instituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico. Medina is also with the School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada. Salvo is also with the Michael & Susan Dell Center for Healthy Living, at the University of Texas Health Science Center at Houston, School of Public Health (Austin Regional Campus). Jáuregui ([email protected]) is corresponding author. 1088
Research on ACS is scarce in Latin America and in low-tomiddle-income countries (LMIC) in general.18–21 Two studies reported negative associations between ACS and overweight22 or adiposity.23 Some studies have identified distance to school20,21 and crime and traffic as correlates of ACS in Latin America.21 In Mexico, ACS among youth has not been studied yet. It is important to identify the correlates of ACS in Mexico, as these may vary from those known for HIC, due to socioeconomic, political and structural differences, as has been reported for other Latin American countries.24,25 Addressing this gap in knowledge is important for future surveillance and monitoring of ACS in Mexico, and to design intervention programs using ACS to increase daily PA. The objective of our study was to analyze the prevalence and correlates of ACS in a nationally representative sample of Mexican adolescents aged 10 to 14 years. A secondary objective was to explore the association between ACS and BMI status.
Methods Design and Participants We used data from the 2012 Mexican National Health and Nutrition Survey (ENSANUT 2012). ENSANUT 2012 used a cross-sectional study design, with a probabilistic multistage stratified cluster sample of Mexican households. It is representative of the 4 regions of the country (North, Central, South and the metropolitan area of Mexico City) and of urban (≥ 2 500 residents) and rural (< 2500 residents) localities. Data were collected between October 2011 and May 2012. A total of 3399 adolescents 10 to 14 years old were interviewed, representing 11,339,401 individuals in this age range. A detailed description of the methodology is published elsewhere.26 All participants provided informed consent before participating. The Ethics Review Board of the National Institute of Public Health of Mexico approved the survey.
Active Commuting to School in Mexican Adolescents 1089
Downloaded by Washington Univ In St Louis on 09/17/16, Volume 12, Article Number 8
Commuting Modes and Travel Times Data on commuting mode to school and travel time (from home to school and back), a proxy for travel distance, were collected from adolescents 10 to 14 years old by a computer-assisted interview. Adolescents received help from their parents/guardians to answer the interview. To assess ACS, relevant survey items from the Health Behavior in School-Aged Children Survey (HBSC) were culturally adapted and pilot tested for in-person administration, during a 2-week period, in a Mexican setting. The HBSC is a reliable tool (Cronbach’s alpha ≥ 0.8) that includes specific items to assess ACS among school-aged children and adolescents.27 Based on this, participants were asked: ‘On a typical day, the longest part of your journey from home to school and from school to home is made by. . . ?’Response choices were 1) walking; 2) bicycle ridden by me; 3) bicycle ridden by somebody else (this option was added to the Mexican HBSC as a result of the findings of the pilot testing phase); 4) bus, metro, or train; 5) car or motorcycle; and 6) other. Per the HBSC scoring protocol,27 ACS was defined as those individuals choosing options 1 and 2, but not 3–6. Travel time was assessed by the following question: In a typical week, how much time do you spend commuting to school? Answers were reported in hours and minutes.
Demographic Variables Demographic information was obtained from the parent/guardian and their child using a questionnaire. Information on mother’s education level and working status were collected. The questionnaire also included items on sociodemographic characteristics of the head of the household, family structure, household characteristics and assets (including car ownership), household consumption patterns (expenditure and rent), and neighborhood poverty. With these, plus additional information on the neighborhood poverty index (from the National Institute of Geography and Statistics), the ENSANUT 2012 generated a household socioeconomic status indicator using a predictive model of income (decile-based), based on the National Income and Expenditure Survey 2010.28 This indicator was found to adequately describe the heterogeneity of standard socioeconomic variables, such as schooling years of the head of the household, income, access to services, and household assets.28
Screen Time and Participation in Organized Physical Activities Screen time and participation in organized activities or sports during the past 12 months were ascertained by questionnaire. Participants responded to the following questions. 1. ‘During the past 12 months, in how many sport teams, individual sports or organized physical activities did you take part in a competitive fashion, or very frequently, such as extracurricular activities or club/varsity teams?’ Response options were 1) none, 2) 1, 3) 2, 4) 3, and 5) 4 or more. 2. ‘In a weekday (Monday-Friday), how many hours do you spend in front of a screen, either watching television, movies, soap operas, or playing videogames?’ Response options were 1) none, 2) 1 to 2 hours, 3) 3 to 4 hours, 4) 5 to 6 hours, 5) 7 to 8 hours, and 6) 9 or more hours. The same question was used to collect weekend screen-time.
BMI Status Weight and height were objectively measured by trained field workers following standardized procedures.29 BMI z-scores (BMI =
kg/m2) were calculated and classified as overweight (between +1 and +2 SD) and as obese (above +2 SD) according to the WHO growth references.30,31
Statistical Analysis Descriptive statistics using frequencies and means (SD) were obtained. Proportions of travel modes and ACS were calculated. Dummy variables for ACS (0 = No, 1 = Yes) and weight status (0 = Normal, 1 = Overweight or obese) were generated. Correlates of ACS were analyzed using bivariate and multivariate logistic regression models. Independent variables included travel time, demographic variables (sex and age), sedentary and PA behavior (taking part in organized activities or sports and screen time), family characteristics (family socioeconomic status and motor vehicle ownership, mother’s education level and working status), and geographic variables (locality and country region). To determine if ACS was associated with BMI, we conducted a series of regression analyses with ACS as the independent variable and z-scored BMI or BMI category (normal vs. overweight or obesity) as the dependent variable. Since it has been suggested that more time spent in ACS may be inversely associated with BMI,32–34 we included an interaction term between commuting mode and travel time to test this relationship. The model was adjusted for the effect of potential confounders (same covariates as previous model). All adjusted models were tested for specification error, goodness of fit and multicollinearity. Differences between unadjusted means and proportions and regression model estimates were considered significant if P < .05 for main effects and P < .1 for interactions.35 All analyses were performed using the survey weights commands in STATA 11.0 (StataCorp, College Station, Texas, USA) (eg, svy) to take into account the complex survey design.
Results Of the 3399 adolescents measured, 94 did not attend school and 354 were excluded for missing data, leaving a sample size of 2952 adolescents in the analysis of ACS correlates, representing 11,417,260 adolescents. No differences were found between the excluded adolescents and the analytical sample. Among the analytical sample, the mean age was 12.0 (±0.9) years old, a total of 52.1% were male, and 51.3% were attending elementary school. Almost 40% of the families owned a motorized vehicle, 62.4% of the mothers reported not working, and 77.7% had an education level lower than high school (Table 1), which is consistent with data from the 2010 Mexican census.
Prevalence of Active Commuting to School A total of 70.8% (walking: 68.8%, bicycling: 2.0%) of the adolescents engaged in ACS, while 14.6% used public transportation, 13.3% were driven to school in a private motorized vehicle, and 1.3% used a combination of travel modes or were ridden to school by somebody else (Table 2). Travel time to school was of 20.2 min (Inactive commuting = 26.3, 95% CI: 24.1–28.8; ACS = 18.1, 95% CI: 17.1–19.2). ACS was more prevalent among male adolescents (74.1%), adolescents from mothers with no education (94.2%), from the lowest socioeconomic status (86.6%), and from families without motorized vehicle ownership (78.6%), in rural areas (82.7%), and in the South region of the country (82.3%) (Table 3).
JPAH Vol. 12, No. 8, 2015
Table 1 Frequency Distributions for Demographic Variables for Mexican Adolescents in 2011–2012; Mexican National Nutrition and Health Survey, 2012 Variable Age (years)b
n
%a
95% CIa
2952
12
11.9–12.0
Sex Male
1528
52.1
49.1–55.2
Female
1424
47.9
44.8–50.9
Participation in structured activities None
1690
56.9
53.7–60.1
1 or 2
1184
40.8
37.7–43.8
78
2.3
1.5–3.1
3 or more
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Screen time Less or equal to 2 hr/d
1034
32.8
30.1–35.6
More than 2 and less than 4 hr/d
1146
39.7
36.8–42.7
More than 4 hr/d
772
27.4
24.7–30.1
Elementary school
1589
51.3
48.3–54.3
Secondary school
1354
48.7
45.7–51.7
1055
35.6
32.6–38.6
School level
Family SES quintile Quintile 1 Quintile 2
685
24
21.5–26.6
Quintile 3
516
16.9
14.6–19.2
Quintile 4
478
15.6
13.7–17.5
Quintile 5
218
7.9
6.3–9.5
No
1973
62.4
59.4–65.5
Yes
979
37.6
34.5–40.6
None
129
5.1
3.8–6.5
Less than high school
2209
72.6
69.8–75.4
High school or more
614
22.3
19.8–24.8
Mother’s working status
Mother’s education level
Motor vehicle in the family No
1726
61
57.9–64.1
1
1024
31.9
29.1–34.6
More than 1
202
7.1
5.6–8.7
Locality Rural (≤ 2500 inhab)
1056
30.3
28.0–32.6
Urban (> 2500 inhab)
1896
69.7
67.4–72.0
Region of the country North
813
18.6
17.2–20.1
Central
1003
32.1
29.8–34.5
South
991
33.8
31.4–36.1
Metropolitan area
145
15.4
13.2–17.6
1782
62.1
59.1–65.0
BMI classification Normal
a b
1090
Overweight
618
21.7
19.1–24.3
Obese
513
16.2
14.0–18.5
Weighted for probability of selection. Frequency, mean, and 95% CI. JPAH Vol. 12, No. 8, 2015
Table 2 Proportion and Median Travel Time by Commuting Mode to School in Mexican Adolescents in 2011–2012; Mexican National Nutrition and Health Survey, 2012 Prevalence n
%a
Transportation mode Walking
2054
68.9
Bicycling
73
Bicycle ridden by somebody else
16
Public transportation Private motorized vehicle Combination of travel modesc
95%
Travel time (min)
CIa
Meana,b
95% CIa,b
66.0–71.8
18.2
17.2–19.3
2.0
1.2–2.8
15.1
9.1–25.0
0.7
0.0–1.5
19.0
16.6–21.9
370
14.6
12.2–17.0
34.1
29.7–39.2
423
13.3
11.4–15.2
20.0
18.0–22.2
15
0.6
0.0–1.1
29.2
24.0–35.5
Weighted for probability of selection. Means and 95% CI derived from log-transformed variable. c Defined as combining an active transportation mode (walking or bicycling) on the way to school plus an inactive transportation mode (private motorized vehicle) on the way back home, or vice versa. a
Downloaded by Washington Univ In St Louis on 09/17/16, Volume 12, Article Number 8
b
Table 3 Weighted Prevalence and Correlates of Active Commuting to School (Walking or Biking) Among Mexican Adolescents in 2011–2012; Mexican National Nutrition and Health Survey, 2012 Unadjusted prevalence/mean
Bivariate
Adjustedb
% or Mean
95% CI
OR
P
OR
P
20.2
18.8–21.7
0.98
