درخت حوزه‌های تخصصی

The pathophysiology of heat stress occurs when the body's environmental and physiological responses exceed its ability to maintain homeostasis. When internal heat signals or external factors raise body temperature beyond a certain limit that the cooling mechanisms can effectively manage, the resulting increase in core temperature triggers response pathways that themselves induce physiological stress. The primary response mechanisms to heat stress include sweating, peripheral vasodilation, and shivering as thermogenic responses—all of which are activated by elevated temperatures and may seem counterproductive to the body's requirements, yet they operate through a neuromuscular and hormonal feedback system. Genetic factors influence individual heat tolerance; for instance, certain populations have variations in heat shock proteins (HSP70) and ion channels (TRPV1, RYR1) that enhance their heat response and tolerance. Additionally, individuals with higher relative VO2 max levels demonstrate greater heat tolerance, as those who are physically trained exhibit more sudomotor activity and effective evaporative cooling compared to untrained individuals. Furthermore, individuals who acclimatize to heat over time develop improved thresholds for heat stress, enabling them to better regulate internal and cardiovascular temperature stresses during exposure. This knowledge is crucial for populations at risk and in situations where physical exertion is required in hot conditions.

Objective : Athlete burnout, which is a multidimensional syndrome and consists of emotional exhaustion, reduced accomplishment, and sport devaluation, is associated with detrimental outcomes to performance and well-being. The aim of this study was to design and validate a machine learning model by combing real-time physiological monitoring and psychological testing to predict and prevent burnout in elite athletes. Method: We recorded multimodal data for 120 national-level athletes (60 males, 60 females) from three sports over a 6-month period; we obtained heart rate variability (HRV), salivary cortisol, sleep measures, and standardized burnout scales. An ensemble model of XGBoost and LSTM architectures had the best predictive performance (AUC-ROC = 0.91), which was significantly better than that of the traditional logistic regression (AUC-ROC = 0.72, p < 0.001). Results : Distinguishing physiological predictors were HRV (β = -0.34, p < 0.001), cortisol awakening response attenuation (β = 0.29, p = 0.001) and deep sleep reduction (β = -0.27, p = 0.001), with the relationships being moderated by TrL (pinteraction < 0.05). In the three-month implementation trial, the system prospectively identified 68% of the burnout cases early on (median lead time = 18 days); it decreased the incidence by 37% relative to the controls (OR = 0.43, 95% CI [0.28, 0.66]). The model had strong temporal stability (AUC drift < 0.02/month), but there is potential for decreased generalizability to recreational athletes and technology-based restricting widespread application. Conclusions : Our results demonstrate the potential for machine learning-empowered combinatory continuous biometric monitoring and psychological screening to operationalize burnout as a preventive (rather than reactive) challenge in elite sports. The model offers coaches and medical teams’ actionable information to intervene in an individualized manner, but future research is needed to examine menstrual cycle effects and to design cost-effective interventions in youth sports systems.