Relationship Between Quality of Life, Level of Physical Activity, Physical Fitness, and Body Composition on the Academic Performance of High School Students in an Integrated Educational System.

Background: Adolescence is a critical period for the development of physical and cognitive health. Understanding how lifestyle and physical health parameters relate to academic performance and quality of life may inform school-based interventions. Purpose: This study aimed to evaluate the relationship between physical activity level (PAL), quality of life (QoL), physical fitness (PF), strength, speed and agility, body composition, and academic performance (AP) in high school students. Research Design: A cross-sectional, correlational study using multiple linear regression models to assess predictive relationships. Study Sample: 365 students (aged 16.93 ± 0.94 years) participated in the study. Data Collection and Analysis: Evaluations included Body Mass Index (BMI); PAL; QoL; PF (handgrip strength, countermovement vertical jump, and agility); and AP. A multiple linear regression was conducted using AP as the dependent variable, with BMI, jump performance, agility, handgrip strength, and PAL scores as predictors. Five additional multiple linear regressions were performed, each with a QoL domain as the dependent variable, and the same set of predictors as in the AP model. Participants' age and sex were included as covariates in all models. Results: Significant predictive capacity was observed for AP (F = 2.22, p = .028, R = 0.31, R² = 0.093) and two QoL domains: physical health (F = 2.32, p = .021, R = 0.28, R² = 0.079) and psychological health (F = 2.32 and p = .021, R = 0.28, R² = 0.079); however, with weak correlation coefficients (0.2 ≤ R <0.4). Only jump performance and age significantly affected the AP model (β = 0.038, p = .014) and the psychological health domain model (β = 0.48, p = .018). Conclusions: The predictors explained 9.3% of the variance in AP and 7.9% of the variance in physical health and psychological health in QoL domains, suggesting that additional factors (e.g., socioeconomic status, dietary habits) may play a role. The findings highlight the importance of multifactorial approaches in future research.