School-based predictive contributors to six-minute walk test performance across pubertal stages: an explainable machine learning study in Tunisian adolescents.

Background: The six-minute walk test (6MWT) provides a practical measure of submaximal functional exercise capacity in adolescents and reflects the integrated response of cardiovascular, respiratory, and musculoskeletal systems during walking. Traditional age-based linear models inadequately capture nonlinear, maturation-dependent interactions among anthropometric, behavioral, and physiological factors. This study aimed to (i) identify stage-specific predictive contributors to 6MWT performance using supervised machine learning, (ii) improve interpretability through explainable artificial intelligence, and (iii) explore data-driven functional and cardiovascular profiles across peak height velocity (PHV) stages. Methods: In this cross-sectional school-based study, 3,166 Tunisian adolescents (11-18 years) were categorized as pre-PHV ( Results: The global model explained 40% of the variance in 6MWT distance (R Conclusions: In this cross-sectional cohort, 6MWT performance showed maturation-stage-specific prediction patterns, but model performance remained modest, particularly before PHV. Physical activity had the strongest model contribution after PHV and may represent an important modifiable correlate, although causal inference cannot be made from these data. The exploratory functional and cardiovascular profiles should be interpreted as data-driven clusters rather than stable biological states. These findings support maturation-sensitive assessment of adolescent 6MWT performance and highlight the need for longitudinal and interventional studies.