Heart rate variability (HRV) is an essential physiological metric offering insights into autonomic nervous system activity and cardiovascular regulation. In sports science, its non-invasive nature has made HRV a valuable tool to monitor training adaptations, athlete recovery states, fatigue management, and overall health and wellness. Although traditional HRV recordings necessitate a five-minute measurement duration, emerging ultra-short-term HRV (UST-HRV) approaches facilitate rapid, real-time physiological tracking. Nevertheless, several challenges remain regarding the validation, reliability, and standardization of UST-HRV methods, as well as optimal integration with advanced analytic techniques and wearable technologies.
This Research Topic aims to provide a comprehensive exploration of recent advancements and applications of HRV analysis in sports science contexts. Specifically, it endeavors to uncover new evidence concerning the reliability and validity of HRV measurement methods, particularly UST-HRV, and to examine innovative analytic strategies that integrate artificial intelligence (AI), including machine learning (ML) models, and wearable sensors. Key inquiry areas include determining effective approaches for leveraging HRV data to optimize athletic performance, accurately monitor training intensity and load, evaluate athletes’ recovery and fatigue states, and implement personalized training and recovery interventions.
To clearly define the scope and boundaries, this Research Topic primarily focuses on research in sports contexts, emphasizing developed and novel techniques in HRV analysis as tools for improved sports performance and athlete management. We welcome submissions addressing the following themes:
• Reliability, accuracy, and standardization of ultra-short-term HRV analysis in sports-related research and in-field applications.
• Monitoring of training load, intensity, and recovery status guided by HRV metrics.
• Personalized HRV-driven training program design and individualized athlete recovery strategies.
• Investigations of environmental, physiological, behavioral, and psychological variables impacting HRV outcomes in athletes.
• Novel signal processing algorithms, artificial intelligence (AI) including machine learning (ML) approaches, enhancing HRV interpretation and real-time monitoring.
• Integration and validation of wearable sensor technologies in tracking HRV dynamics within athletic populations.
Keywords: Heart Rate Variability, Ultra-Short-Term HRV, Sports Performance Monitoring, Training Load Assessment, Wearable Technology, Athlete Recovery, Exercise Physiology
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
