# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
## 4 Discussion
The algorithm's rapid processing time (averaging 1.23 \(\pm\) 0.18 s per session) enables real-time feedback applications, which could significantly enhance the practical utility of heart rate monitoring during HIIT training. This processing efficiency aligns with recent technological advancements in wearable sensors and real-time monitoring [1; 2]. Compared to traditional manual analysis methods that require substantial time investment and expert interpretation [20], our automated approach provides an immediate objective assessment with minimal technical expertise required. This makes sophisticated heart rate analysis more accessible to coaches, practitioners, and healthcare providers working with diverse populations, supporting the integration of technology in sports science [3]. A key strength of our approach is its ability to track these parameters longitudinally, creating individual historical records that enable assessment of training progress over time. By establishing baseline values for each athlete and monitoring changes in heart rate parameters across multiple sessions, trainers can make data-driven decisions about training prescription and progression. This personalized approach aligns with contemporary trends toward individualized training optimization and precision in sports science [25; 26]. The application of algorithmic analysis to physiological data represents a growing trend in sports technology, particularly in integrating artificial intelligence with wearable devices for cardiovascular health management [26] and fatigue monitoring [27]. The physiological basis for our findings is supported by extensive research on HIIT's cardiovascular effects. When considering the comparative effectiveness of HIIT and continuous training modalities such as MICT, it is essential to acknowledge the methodological challenges inherent in such comparisons. Recent meta-analyses have demonstrated that outcomes are highly dependent on how variables such as total work, exercise duration, and intensity are controlled across conditions [12; 17]. Other research emphasized the importance of monitoring intensity to maximize these benefits [13]. Our algorithm provides a practical method for quantifying these responses in real time, potentially enhancing HIIT's effectiveness across various populations, including clinical settings where HIIT has shown promising health benefits [11; 15; 18]. Several limitations should be acknowledged when interpreting our findings. First, our study included a relatively homogeneous sample of young, healthy university students with previous training experience, potentially limiting the generalizability to other populations, such as clinical patients or elite athletes. Second, while heart rate is a valuable indicator of cardiovascular strain, it represents only one aspect of the complex physiological response to HIIT. Research suggests that integrating multiple sensor types to monitor both internal and external workload would provide a more comprehensive assessment [8]. Future research should incorporate additional physiological parameters, such as oxygen consumption, blood lactate, or muscle oxygenation, to provide a more holistic evaluation of training responses. Third, our algorithm was tested specifically in indoor cycling HIIT protocols, and its performance in other exercise modalities or training structures requires further validation, considering the diverse applications of HIIT [16; 18].