Dataset Information

Parent's sociodemographic factors, physical activity and active commuting are predictors of independent mobility to school.

ABSTRACT:

Background

Independent mobility (IM) provides young people with many opportunities to increase their autonomy and physical activity (PA). This study aimed to analyse whether the parent's PA, active commuting to work and sociodemographic factors serve as predictors of IM to school in children and adolescents.

Methods

A total of 684 parents (52.8% mothers) and their offspring (56.4% girls) were included in this study, which was performed in Granada (Spain) and Valparaíso (Chile). The parents self-reported their sociodemographic characteristics, PA and mode of commuting to work. The mode of commuting to and from school and the offspring accompaniment mode were reported. T-test and chi-square test were used to study quantitative and qualitative differences by parental gender, respectively. Binary logistic regression models (odds ratio = OR) and stepwise analysis were performed to study the association between the parents' sociodemographic variables and IM to school.

Results

Adolescents showed higher IM to school than children (58.9% vs 40.2%; p < 0.001). No car availability and shorter distance to work were positively associated with higher IM to school in children (OR = 2.22 and 2.29, respectively). Mothers' lower salary/month (OR = 2.75), no car availability (OR = 3.17), and mother passive commuting to work (OR = 2.61) were positively associated with higher IM to school in adolescents. The main predictor of IM to school in children and adolescents was no car availability (OR = 6.53).

Conclusion

Parental sociodemographic factors, such as salary, distance to work and car availability, were associated more strongly with IM than parental PA and active commuting to work.

SUBMITTER: Rodriguez-Rodriguez F

PROVIDER: S-EPMC8180041 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and Objectives: Active commuting to and from university (ACU) could be a strategy to increase physical activity levels (PA) and promote health in young university students. We aimed to a) examine the patterns of commuting to university in Chilean students; b) the association between the mode of commuting to and from university and socio-demographic factors and PA-levels. Materials and Methods: A total of 496 university students (21.6 ± 2.4 years old) from two universities from Valparaíso (central coast of Chile) participated in this study. Personal data, home address, socio-economic status, PA, and the usual mode of commuting to and from the university were self-reported by a questionnaire. The commute distances were objectively measured using Google-Maps-software. Associations were examined using binary logistic regressions. Results: The main mode of commuting was by bus (to university: 55.2% vs. from university: 59.3%; p < 0.001). The least used mode was cycling (1.4% to and from university). Students living >5-km from university were less active commuters than those living in closer distances: (2-5 km, odds ratio (OR): 4.424, 95% and 95% confidence intervals (CI): 2.443-8.011, p < 0.001; 2 km, OR: 143.052, 95% CI: 55.154-371.030, p < 0.001). Students with low PA-levels were less active commuters than those with medium (OR: 1.446; 95% CI: 0.864-2.421; p = 0.160) or higher levels (OR: 1.880; 95% CI: 1.880-1.094; p = 0.022). Students who lived between 2 and 5 km, presented a significant association to be active commuters when they showed medium PA-levels (OR: 5.244, 95% CI: 1.358-20.246; p = 0.016). Conclusions: Chilean university students from Valparaíso are mainly passive commuters using public transport as the main mode of commuting to and from university; longer distances from home to the university are associated with low PA levels. ACU in distances between 2-5 km (mainly walking) could contribute to having medium PA-levels in Chilean university students. Thus, promoting the ACU walking to and from the university in such distances could be an effective strategy to increase the overall PA levels in Chilean university students.

Project description:Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

Project description:BackgroundChildren's independent mobility differs between groups of adolescents, but knowledge is lacking on how mobility-limiting factors interact. This study explores the association between factors that can affect young adolescents' mobility, searching for typical patterns within a geographical area where mobility is both relatively high and promoted (in this case Stockholm County, Sweden). An additional question is how clusters of limiting factors and demographic attributes relate to active commuting to school.MethodsA sample of 7th grade students (ca 13-14 years old) in Stockholm County, Sweden, answered a survey (n = 1008). A cluster analysis was performed on variables descriptive of the respondents and of potential limitations to their independent mobility, such as fears, coping, traffic situation in the neighbourhood and parent/child opinions on mobility (18 variables and 50 categories). Active commuting to/from school was compared using proportion (with 95% confidence intervals) by cluster.ResultsFive consistent and distinct clusters were identified. Among the most discriminating factors were fears experienced in the neighbourhood, strategies to cope with fear, type of housing and traffic environment. Girls were over-represented in the two clusters most typical of respondents experiencing fears (either several of these or darkness in particular) and boys in two others where housing (house vs. apartment) and neighbourhood conditions played a more determinant role. The proportion of active commuting among respondents was quite similar over clusters but was nonetheless higher in the cluster (over girls) reporting more fears and other factors limiting mobility.ConclusionsWhereas fears--and coping--are more typical of adolescent girls in the formation of the clusters, household and neighbourhood characteristics are more typical of boys. Broadly speaking, there seem to be two groups of girls with fears but these differ based on types of fear, ways of coping with fear and their living conditions. The association between the limitations to mobility and active commuting is unclear, the latter being higher among those disclosing a broader range of limiting factors, including fears.