Real-time AI-Driven cadence optimization in elite 800-m runners: a bioenergetic digital twin approach under metabolic constraints.

backgroundThe 800-m track event represents a metabolic paradox in which runners must optimize the depletion rate of finite anaerobic work capacity, W', while avoiding premature estimated metabolic threshold onset. Traditional lactate monitoring suffers from temporal lag, creating a "metabolic black box" that prevents real-time tactical adjustments.

objectiveThis study investigated whether real-time bioenergetic digital twin technology could predict W' depletion trajectories and optimize cadence strategies in elite 800-m runners under competitive metabolic constraints.

methodsTwelve elite collegiate 800-m runners completed three experimental trials: laboratory W' quantification via a 3-minute all-out test, an instrumented 800-m time trial with multi-modal biosensing, and an RBDT-guided race simulation. A physiological model-based neural network integrated real-time muscle oxygenation, cadence, and velocity data to estimate instantaneous W' expenditure. The primary outcome was the correlation between predicted and observed performance collapse points, defined operationally as velocity decrement greater than 5%.

resultsThe RBDT model achieved high predictive accuracy for W' depletion dynamics, with R² = 0.92 and RMSE = 2.88%. Athletes following RBDT-guided cadence adjustments at the critical 500-m node demonstrated 3.2% faster finishing times compared with self-paced trials, with delayed estimated metabolic threshold onset. SHAP analysis showed that feature importance was not static across the race: velocity and acceleration dominated early prediction, SmO₂-derived metrics became most influential during the 400-600 m tactical decision phase, and cadence-related variables increased in importance during the terminal 600-800 m phase.

conclusionsReal-time metabolic monitoring via RBDT may support precision pacing strategies that maximize W' utilization while reducing premature performance collapse. The results support a transition from experience-based to data-informed tactical decision-making in middle-distance running. However, findings should be interpreted in light of the small sample size, instrumented time-trial setting, and possible psychological effects of audio feedback.