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Moderating Influence of Home Location and School Type across Time on Cardiometabolic Risk and Active School Commuting: A Five-Year Longitudinal Study.

Ryan Donald BurnsAna Paula SehnCaroline BrandJoão Francisco de Castro SilveiraCézane Priscila Reuter
Published in: Childhood obesity (Print) (2022)
Background: We examined the moderating influence of home location and school type across time on cardiometabolic risk and active school commuting over 5 years in a sample of children from southern Brazil. Methods: We recruited a sample of children ( n  = 154; baseline age = 9.6 ± 1.5 years old; 56.8% female) who were followed for 5 years from 2011/2012 to 2016/2017. We collected home location, school type, and school commute data using self-report methods and collected cardiometabolic risk measures to calculate a cardiometabolic composite risk score (cMetSyn). General and generalized linear mixed effects models were employed to examine the moderating influence of home location and school type across time on cardiometabolic risk and active school commuting. Results: We found a significant three-way home location × school type × time interaction on cMetSyn scores ( b  = 0.62, 95% confidence interval [CI]: 0.13-1.12, p  = 0.014), indicating that children who were living within rural areas and enrolled in state schools during 2016/2017 had higher cardiometabolic risk compared with children enrolled in municipal schools and living in urban areas at the end of the study. Additionally, we found that children living in rural areas had an 86% lower rate of active school commuting compared with students living in urban areas (rate ratio = 0.14, 95% CI: 0.07-0.32, p  < 0.001). Conclusions: The results suggest that Brazilian children enrolled in state schools and living in rural areas had higher cardiometabolic risk scores at the end of the study and that southern Brazilian children residing in rural areas had a much lower rate of actively commuting to school.
Keyphrases
  • physical activity
  • mental health
  • high school
  • young adults
  • healthcare
  • social support
  • wastewater treatment
  • risk assessment
  • artificial intelligence
  • neural network