Active school commuting among Spanish youth: Social inequalities, environment, and health implications 🚶‍♀🚴🏿‍♂️️️🏫

E. Vidal-Tortosa, P. Campos-Garzón, A. Ruiz-Alarcón, J. Molina-García, P. Chillón

🎯 Introduction

Rationale

Most adolescents don’t get enough physical activity (PA)—especially those from disadvantaged groups (Aznar et al. 2024; Kuhn et al. 2021; Owen et al. 2022).

Active commuting to and from school (ACS) is a simple, low-cost way to boost PA (Campos‐Garzón et al. 2023), particularly for those with limited access to structured options.

Yet, we know little about how disadvantaged groups engage in ACS or how neighbourhood environments influence it (Medeiros et al. 2021).

Study Aims

– To examine the relationship between socioeconomic status (SES), migration background, and both usual mode and weekly frequency of ACS

– To assess how home-neighbourhood environmental characteristics shape these relationships

📊 Data & Methods

Study Design

Cross-sectional study of 366 urban adolescents in Spain, using data from the PACO study—a school-based project promoting cycling among adolescents (Palma Chillón et al. 2021).

Data Source

– Self-reported questionnaire on active commuting

– Linked with geospatial data on home-neighbourhood environments

Analytical Approach

– Descriptive statistics and multilevel regression (individuals nested in schools)

– Two-step modelling:

🔹 Step 1: Adjusted for sociodemographic variables

🔹 Step 2: Added environmental characteristics to explore contextual influences

📈 Results – Descriptive Statistics

Table 1: Descriptive data of the sample

Variable	n (Valid Cases)	% / Mean ± SD
Commuting behaviour
Usual mode of commuting to school	356	Active: 61.8% Passive: 38.2%
Usual mode of commuting from school	356	Active: 65.4% Passive: 34.6%
Weekly frequency of active commuting to school	366	2.81 (2.18)
Weekly frequency of active commuting from school	366	2.83 (2.12)
Sociodemographic characteristics
Age (years)	364	14.35 (0.63)
Gender	366	Female: 54.1% Male: 45.9%
Socioeconomic status	355	Low: 20.9% Middle: 35.0% High: 44.1%
Migration background	352	Yes (≥1 parent foreign-born): 15.6% No (both parents born in Spain): 84.4%
Environmental characteristics
Home-school distance (meters)	344	3650.94 (8240.22)
Intersection density (intersections/km²)	345	208.78 (96.35)
Residential density (residents/km²)	307	11639.93 (5590.39)
Land use mix (index)	337	0.42 (0.13)
Pedestrian infrastructure (points)	332	7.45 (2.05)
Perceived environment (points)	362	11.92 (2.24)

📈 Results – Descriptive Statistics

Figure 1: Usual mode of commuting to and from school by social groups

📈 Results – Descriptive Statistics

Figure 2: Mean standardized scores of weekly frequency of active commuting (n° trips) to and from school and the environmental variables by social groups

🧮 Results – Regression Models

Table 2: ORs and 95% CIs for the associations between sociodemographic and environmental characteristics and usual mode of commuting to school (1 = active, 0 = passive) and weekly frequency of active commuting (n° trips) to school

	Usual mode of commuting to school		Weekly frequency of active commuting (n° trips) to school
Variable	OR (95% CI) (adjusted for sociodemographic characteristics)	OR (95% CI) (adjusted for sociodemographic and environmental characteristics)	OR (95% CI) (adjusted for sociodemographic characteristics)	OR (95% CI) (adjusted for sociodemographic and environmental characteristics)
Sociodemographic characteristics
Age	0.96 (0.75, 1.24)	1.25 (0.78, 2.01)	0.98 (0.92, 1.05)	1.02 (0.95, 1.09)
Gender: Male	1.61 (0.97, 2.67)	2.95 * (1.29, 6.75)	1.07 (0.94, 1.22)	1.15 (0.99, 1.33)
Socioeconomic status: Middle	0.67 (0.33, 1.35)	0.65 (0.21, 2.04)	0.86 (0.73, 1.02)	0.90 (0.74, 1.08)
Socioeconomic status: High	0.48 * (0.25, 0.94)	0.49 (0.16, 1.52)	0.80 ** (0.68, 0.94)	0.94 (0.78, 1.12)
Migration background: No	0.51 (0.25, 1.06)	0.52 (0.17, 1.56)	0.91 (0.76, 1.07)	1.00 (0.83, 1.21)
Environmental characteristics
Home-school distance		0.12 *** (0.06, 0.27)		0.79 *** (0.73, 0.87)
Residential density		1.97 * (1.09, 3.58)		1.32 *** (1.17, 1.49)
Land use mix		1.50 (0.84, 2.67)		1.15 ** (1.04, 1.27)
Pedestrian infrastructure		1.06 (0.72, 1.58)		1.00 (0.93, 1.07)
Perceived environment		1.55 * (1.05, 2.30)		1.07 (1.00, 1.16)
Model Fit Statistics
N	348	260	356	267
AIC	429,76	213,95	1614,01	1100,12
BIC	456,73	256,67	1641,14	1143,16
R2 (total)	0,24	0,72	0,24	0,51

🧮 Results – Regression Models

Table 3: ORs and 95% CIs for the associations between sociodemographic and environmental characteristics and usual mode of commuting from school (1 = active, 0 = passive) and weekly frequency of active commuting (n° trips) from school

	Usual mode of commuting from school		Weekly frequency of active commuting (n° trips) from school
Variable	OR (95% CI) (adjusted for sociodemographic characteristics)	OR (95% CI) (adjusted for sociodemographic and environmental characteristics)	OR (95% CI) (adjusted for sociodemographic characteristics)	OR (95% CI) (adjusted for sociodemographic and environmental characteristics)
Sociodemographic characteristics
Age	0.92 (0.71, 1.19)	1.25 (0.75, 2.10)	0.96 (0.90, 1.03)	1.00 (0.94, 1.08)
Gender: Male	1.27 (0.76, 2.12)	1.79 (0.76, 4.21)	1.07 (0.94, 1.22)	1.14 (0.98, 1.31)
Socioeconomic status: Middle	0.53 (0.25, 1.10)	0.41 (0.12, 1.45)	0.82 * (0.69, 0.97)	0.86 (0.71, 1.03)
Socioeconomic status: High	0.43 * (0.21, 0.85)	0.36 (0.10, 1.28)	0.79 ** (0.67, 0.93)	0.93 (0.78, 1.12)
Migration background: No	0.44 * (0.20, 0.96)	0.40 (0.12, 1.39)	0.88 (0.75, 1.05)	1.02 (0.85, 1.23)
Environmental characteristics
Home-school distance		0.08 *** (0.03, 0.20)		0.79 *** (0.72, 0.86)
Residential density		1.75 (0.97, 3.13)		1.43 *** (1.26, 1.62)
Land use mix		0.82 (0.49, 1.37)		1.17 ** (1.06, 1.30)
Pedestrian infrastructure		0.74 (0.48, 1.15)		0.96 (0.89, 1.03)
Perceived environment		1.83 ** (1.20, 2.80)		1.06 (0.98, 1.14)
Model Fit Statistics
N	348	261	356	267
AIC	411,59	188,04	1571,7	1047,34
BIC	438,56	230,82	1598,82	1090,39
R2 (total)	0,27	0,75	0,25	0,58

🧮 Results – Regression Models

Figure 3: ORs and 95% CIs for the associations between socioeconomic status and migration background with usual mode of commuting (1 = active, 0 = passive) and weekly frequency of active commuting (n° trips) to and from school. Estimates are adjusted for sociodemographic and environmental covariates

🧠 Discussion – Social Inequalities in ACS

Disadvantaged adolescents (low SES, migration background) were more likely to actively commute

– Builds on earlier Spanish evidence (P. Chillón et al. 2009); aligns with international studies (Pinto et al. 2017; Pont et al. 2009; Rothman et al. 2018; Sirard and Slater 2008)

– Trip direction matters:

🔹 Stronger social patterning for commuting from school

🔹 Likely reflects greater access to passive transport among advantaged adolescents in the afternoon

– ACS may help reduce PA inequalities, especially where other activity options are limited

– But this may reflect necessity rather than choice — a result of constrained transport access (Salvo et al. 2023)

🌍 Discussion – Environmental Mediation & Implications

Environmental factors may explain social inequalities in ACS

– Including environmental variables removed the SES and migration background effects

– Home–school distance was the strongest predictor

– Other factors (density, land use mix, perceived environment) showed modest effects

– Pedestrian infrastructure not associated → limited influence in this context

Policy Implications

– Promote ACS to reduce PA inequalities, with support for those who already rely on it

– Focus on afternoon travel, where inequalities are sharper

– Enhance walking conditions to improve safety and experience for those walking out of necessity

✅ Conclusions

– Disadvantaged groups engaged more in ACS: higher-SES students used active modes less and made fewer trips (both directions); students without migratory background used active modes less from school.

– Differences disappeared after adjusting for home-neighbourhood environment; home–school distance was the strongest factor.

– Other environmental features (residential density, land use mix, perceived environment) showed weak/inconsistent links; pedestrian infrastructure not significant.

– ACS may help compensate for disparities in structured PA

– Environmental context helps explain social gradients in ACS

– Policy should prioritise short home–school distances and safe, supportive conditions for those who rely on ACS, particularly for afternoon travel

– Equity-focused support for ACS may reduce PA inequalities and promote transport justice and health equity

References

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Campos‐Garzón, Pablo, Javier Sevil‐Serrano, Antonio García‐Hermoso, Palma Chillón, and Yaira Barranco‐Ruiz. 2023. “Contribution of Active Commuting to and from School to Device‐measured Physical Activity Levels in Young People: A Systematic Review and Meta‐analysis.” Scandinavian Journal of Medicine & Science in Sports 33 (11): 2110–24. https://doi.org/10.1111/sms.14450.

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