Digital health interventions from the humanistic perspective of sports: strategies to promote health for all

The proposed model achieved a 35% relative reduction in Mean Absolute Error (MAE) compared to competitive machine learning and deep learning baselines. In classification tasks, it improved Accuracy and F1-score by 9-12%, reaching an Accuracy of 95.1% and an F1-score of 94.7%. The proposed framework establishes a new benchmark for personalized digital health interventions by combining predictive accuracy and humanistic fairness. It demonstrates the feasibility of delivering individualized exercise strategies at scale, while ensuring equitable health promotion across diverse populations.