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Bi-directional associations between physical activity and growth indicators of pre-school aged children.

Otávio Amaral de Andrade LeãoThaynã Ramos FloresJaime BarrattAndréa Dâmaso BertoldiMarlos Rodrigues DominguesJohn CairneyUlf EkelundInácio Crochemore Mohnsam da SilvaGregore Iven MielkePedro Rodrigues Curi Hallal
Published in: Journal of sports sciences (2023)
Physical activity for young children provides a wealth of benefits for health and development. However, little is known about the inter-relationship of physical activity and growth indicators. The aim of this study was to test the bi-directional associations of physical activity and growth indicators in children under five years of age. This prospective study included 1,575 children with data on physical activity and growth indicators at ages 12, 24 and 48 months. Accelerometers were used to measure physical activity. Z-scores for length/height-for-age, weight-for-length/height, weight-for-age and body mass index (BMI)-for-age were calculated. Bi-directional associations between physical activity and growth indicators were evaluated using cross-lagged panels based on Generalized Estimating Equations and cross-lagged structural equation models. Physical activity was consistently associated with lower weight-related growth indicators: BMI-for-age: β=-0.12; Weight-for-age: β=-0.11; Weight-for-length/height: β=-0.12. Higher BMI-for-age indicated lower physical activity (β=-0.06). When the exposure was lagged, the association of physical activity on weight-related growth indicators remained, but weight-related growth indicators showed a negative association on physical activity. A bi-directional association between physical activity and weight-related growth indicators was observed. The magnitude of associations were stronger when physical activity was modelled as exposure. These results reinforce the importance of physical activity since early years.
Keyphrases
  • physical activity
  • body mass index
  • weight gain
  • sleep quality
  • healthcare
  • weight loss
  • young adults
  • public health
  • mental health
  • artificial intelligence
  • deep learning
  • social media