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Correlates of Children's Independent Mobility in Canada: A Multi-Site Study.

Negin Alivia RiaziSébastien BlanchetteFrançois TrudeauRichard LaroucheMark S TremblayGuy Faulkner
Published in: International journal of environmental research and public health (2019)
Globally, physical inactivity is a concern, and children's independent mobility (CIM) may be an important target behavior for addressing the physical inactivity crisis. The aim of this study was to examine correlates of CIM (8-12 years old) in the Canadian context to inform future interventions. CIM was measured via parent surveys. Individual, social, and environmental correlates of CIM were examined using a social-ecological framework. 1699 participants' data were analyzed using descriptive statistics and gender-stratified linear mixed-effects models while controlling for site, area-level socioeconomic status, and type of urbanization. Individual correlates including child grade (β = 0.612, p < 0.001), language spoken at home (β = -0.503, p < 0.001), car ownership (β = -0.374, p < 0.05), and phone ownership (β = 0.593, p < 0.001) were associated with CIM. For boys, parental gender (β = -0.387, p < 0.01) was negatively associated with CIM. Parents' perceptions of safety and environment were significantly associated with CIM. Location (i.e., site) was significantly associated with CIM (ref: Trois-Rivières; Ottawa (β = -1.188, p < 0.001); Vancouver (β = -1.216, p < 0.001)). Suburban environments were negatively associated with boys' independent mobility (β = -0.536, p < 0.05), while walkability (400 m β = 0.064, p < 0.05; 1600 m β = -0.059, p < 0.05) was significantly associated with girls' independent mobility only. Future research and interventions should consider targeting "modifiable factors" like children's and parents' perceptions of neighborhood safety and environment.
Keyphrases
  • mental health
  • physical activity
  • healthcare
  • young adults
  • primary care
  • current status
  • cross sectional
  • autism spectrum disorder
  • big data
  • data analysis