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Modeling the Dynamics of Children's Musculoskeletal Fitness.

Ana ReyesRaquel ChavesMaria Olga VasconcelosSara PereiraGo TaniDavid F StoddenDonald HedekerJosé António Ribeiro MaiaAdam D G Baxter-Jones
Published in: International journal of environmental research and public health (2023)
This study models children's musculoskeletal fitness (MSF) developmental trajectories and identifies individual differences related to effects of time-invariant, as well as time-varying covariates. A total of 348 Portuguese children (177 girls) from six age cohorts were followed for three years. MSF tests (handgrip strength, standing long jump and shuttle run), age, body mass index (BMI), socioeconomic status (SES), gross motor coordination (GMC) and physical activity (PA) were assessed. Data were analyzed using multilevel models. Between 5 and 11 years of age, boys outperformed girls in all three MSF tests ( p < 0.05). Birth weight was positively associated with shuttle run performance ( β = -0.18 ± 0.09, p < 0.05). BMI was positively associated with handgrip strength ( β = 0.35 ± 0.04, p < 0.001) and shuttle run performance ( β = 0.06 ± 0.01, p < 0.001), but negatively associated with standing long jump performance ( β = -0.93 ± 0.23, p < 0.001). GMC was positively associated ( p < 0.001) with all three MSF tests, while PA was associated with standing long jump ( β = 0.08 ± 0.02, p < 0.05) and shuttle run ( β = -0.003 ± 0.002, p < 0.05) performance only. No school environment effects were found, and SES was not related to any MSF tests. Children's MSF development showed a curvilinear shape with increasing age, with boys outperforming girls. Weight status and physical behavior characteristics predicted MSF development, while environmental variables did not. Examining potential longitudinal predictors of MSF across multiple dimensions is important to gain a more holistic understanding of children's physical development as well as to future interventions.
Keyphrases
  • physical activity
  • body mass index
  • young adults
  • weight gain
  • birth weight
  • mental health
  • depressive symptoms
  • gestational age
  • big data
  • machine learning
  • cross sectional
  • artificial intelligence