Multidimensional Data Integration and AI Approaches for Training Load Management, Performance Optimization, and Injury Risk Reduction in Sport

Sport science and performance analytics increasingly rely on athlete-monitoring technologies to quantify training and competition demands. Data from GPS, accelerometry, heart-rate monitors, wellness questionnaires, neuromuscular assessments, physiological tests, and injury surveillance systems can characterize both external and internal load. However, the volume, heterogeneity, and longitudinal nature of these data create challenges for interpretation and practical decision-making. Isolated variables and fixed thresholds may inadequately represent the complex interactions among training load, fatigue, recovery, adaptation, performance, and injury risk. Recent research has demonstrated the potential of multidimensional monitoring and artificial intelligence (AI) and machine learning (ML) to identify nonlinear relationships, detect individual response patterns, and improve prediction. Nevertheless, questions remain regarding model validity, generalizability, interpretability, data quality, and integration into real-world high-performance environments. Further investigation is needed to establish robust, athlete-centred approaches that translate complex data into actionable decisions.This Research Topic aims to advance the collection, integration, analysis, and interpretation of multidimensional athlete-monitoring data for training-load management, performance optimization, readiness assessment, and injury-risk reduction. It will examine how AI, ML, and related data-driven methods can improve understanding of individual responses to training stimuli and support more precise, adaptive, and evidence-informed decision-making. Contributions may address the development and validation of predictive or explanatory models, the integration of diverse data streams, and the practical implementation of analytical approaches in sport. Particular emphasis is placed on identifying methods that are reliable, interpretable, ethically responsible, and useful to athletes, coaches, sport scientists, and medical practitioners.To gather further insights into multidimensional, longitudinal, and applied approaches to athlete monitoring, this Research Topic welcomes work conducted across sports, performance levels, and training or competition settings, while acknowledging differences in technology availability, sample size, and data quality. Themes include, but are not limited to:Multidimensional athlete monitoring and data integrationAI- and ML-based approaches to training-load managementIndividualized load–response modellingFatigue, recovery, readiness, and adaptation assessmentPerformance analysis and optimizationInjury-risk prediction and injury-prevention supportIntegration of external load, internal load, wellness, and contextual dataLongitudinal modelling and personalized athlete profilesValidation, interpretability, and practical implementation of predictive modelsData quality, privacy, governance, and ethical considerations in athlete monitoringApplied decision-support systems for coaches and performance practitionersAppendix: We welcome original research articles, systematic reviews and meta-analyses, and applied case studies. Topic editors: Víctor Toro-Román, Javier Pecci, David Lobo-Triviño, Jose Carlos Ponce-Bordón Source: https://www.frontiersin.org/research-topics/85901/multidimensional-data-integration-and-ai-approaches-for-training-load-management-performance-optimization-and-injury-risk-reduction-in-sport