Nonlinear association between physical activity and bone mineral density in adolescents

This study aimed to examine the association between physical activity (PA) and bone mineral density (BMD) and to explore potential non-linear and threshold relationships in adolescents aged 12-19 years.

Data from the National Health and Nutrition Examination Survey 2005-2010 were used for this cross-sectional analysis. Statistical analyses included weighted multivariate linear regression, restricted cubic spline (RCS) analysis, threshold effect evaluation, and sensitivity analyses.

After adjusting for covariates, adolescents with low PA had significantly lower BMD at both the lumbar spine and femoral neck compared to those with moderate activity levels (lumbar spine: β = -22.79, 95% CI: -37.86, -7.71, p = 0.005; femoral neck: β = -22.93, 95% CI: -43.66, -2.20, p = 0.032). High PA levels were not significantly associated with BMD (lumbar spine: p = 0.542; femoral neck: p = 0.226). RCS analyses revealed significant nonlinear associations between PA and BMD at both lumbar spine (p = 0.035) and femoral neck (p < 0.001). Threshold analysis showed that PA levels below 3500 MET-min/week were associated with higher BMD, but this association diminished as activity levels exceeded this threshold.

This study identified a nonlinear association between PA and BMD in adolescents, with evidence of a threshold effect around 3500 MET-min/week.

This study demonstrates a nonlinear association between physical activity and bone mineral density (BMD) in adolescents, indicating that increases in physical activity are beneficial to BMD only up to a certain level. A threshold of approximately 3500 MET-min/week was identified, beyond which additional physical activity was not associated with further gains in BMD, suggesting the presence of an optimal range of activity for adolescent bone health. Using data from adolescents in the nationally representative NHANES 2005-2010 dataset, the study applied weighted regression, spline modeling, and sensitivity analyses to ensure reliable, generalizable results.