Mapping HRV in sports science: from monitoring to machine learning.

Background: Heart Rate Variability (HRV) is a crucial non-invasive marker of autonomic nervous system function, extensively applied in sports science for monitoring training load, fatigue, recovery, and performance optimization. The rapid expansion and diversification of HRV research necessitate a comprehensive bibliometric analysis to map the knowledge structure and emerging trends. Objective: This study employed innovative bibliometric visualization to quantitatively analyze the literature landscape, research hotspots, and evolutionary trends in HRV applications within sports from 2010 to 2025. It aimed to identify key contributors, delineate major research themes, uncover nascent directions, and identify emerging research trajectories. Method: Utilizing CiteSpace 6.3.R1 and VOSviewer, we conducted a comprehensive visual analysis of 1,660 articles retrieved from the Web of Science Core Collection and Scopus databases. This study performed co-authorship, co-citation, keyword co-occurrence, cluster analysis, and burst detection to unveil publication trends, collaborative networks, influential works, core authors, research hotspots, and emerging trends. Results: Publication volume showed a significant growth trend, peaking in 2022 with 209 articles. The USA and Brazil were the most productive countries, with the University of São Paulo leading institutionally. Document co-citation analysis identified five major research hotspots: Athlete Monitoring, Biofeedback, Sport-related Concussion, Anxiety, and Endurance Exercise. Keyword burst analysis revealed three dominant future trends: "Sleep," "Machine Learning," and "Anxiety". Conclusion: This bibliometric analysis delineates the evolution of HRV research in sports, confirming established domains while highlighting the importance of HRV's role in concussion management and psychological assessment. Critically, it highlights the field's evolving trajectory, emphasizing the growing integration of sleep interactions, machine learning-driven personalization, and the dynamics of HRV and anxiety. These findings provide a structured roadmap for future research and practical applications.