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Youth Sport Participation by Metropolitan Status: 2018-2019 National Survey of Children's Health (NSCH).

Ashleigh M JohnsonJason N BocarroBrian E Saelens
Published in: Research quarterly for exercise and sport (2022)
Purpose: This cross-sectional study used data from the 2018-2019 National Survey of Children's Health to examine the association between metropolitan statistical area (MSA) status and sports participation among American youth ages 6-17. Methods: Weighted prevalence statistics were computed for sports participation by MSA status (non-MSA, MSA), overall and by child sex and age. Modified Poisson regression was used to estimate prevalence ratios (PR) for non-MSA versus MSA youth, before and after adjusting for special health-care needs, race/ethnicity, household income, parent education, and family structure. Results: The final sample included 30,029 youth [M age  = 11.6 years (SD = 0.4), 51.4% female, 49.0% White]. About 56% participated in sports in the past year. Sports participation was significantly higher among females versus males [59.1% (95% CI: 57.4%-60.7%) versus 52.1% (95% CI: 50.4%-53.8%), p < .001]. Among ages 6-11, those in non-MSAs (versus MSAs) were less likely to participate in sports [PR 0.92 (95% CI: 0.86-0.99), p = .033], which was non-significant after adjustment. In adjusted models, youth ages 12-17 in non-MSAs (versus in MSAs) were more likely to participate in sports overall [aPR 1.07 (95% CI: 1.00-1.15), p = .042] and among males [aPR 1.12 (95% CI: 1.01-1.23), p = .026]. Conclusion: The relationship between MSA status and sports participation may be largely driven by factors that affect youth's ability to participate in sports. Sports participation was higher among females versus males overall. In the models adjusted for demographics, non-MSA youth ages 12-17 were more likely to participate, particularly males. Efforts promoting youth sports should consider differences in socio-demographic factors between MSA versus non-MSA areas to help increase participation.
Keyphrases
  • physical activity
  • mental health
  • young adults
  • healthcare
  • public health
  • high school
  • magnetic resonance
  • computed tomography
  • health information
  • machine learning
  • climate change
  • magnetic resonance imaging
  • big data