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Purpose: Training characteristics such as duration, frequency, and intensity can be manipulated to optimize endurance performance, with an enduring interest in the role of training-intensity distribution to enhance training adaptations. Training intensity is typically separated into 3 zones, which align with the moderate-, heavy-, and severe-intensity domains. While estimates of the heavy- and severe-intensity boundary, that is, the critical speed (CS), can be derived from habitual training, determining the moderate–heavy boundary or first threshold (T1) requires testing, which can be costly and time-consuming. Therefore, the aim of this review was to examine the percentage at which T1 occurs relative to CS. Results: A systematic literature search yielded 26 studies with 527 participants, grouped by mean CS into low (11.5 km·h−1; 95% CI, 11.2–11.8), medium (13.4 km·h−1; 95% CI, 11.2–11.8), and high (16.0 km·h−1; 95% CI, 15.7–16.3) groups. Across all studies, T1 occurred at 82.3% of CS (95% CI, 81.1–83.6). In the medium- and high-CS groups, T1 occurred at a higher fraction of CS (83.2% CS, 95% CI, 81.3–85.1, and 84.2% CS, 95% CI, 82.3–86.1, respectively) relative to the low-CS group (80.6% CS, 95% CI, 78.0–83.2). Conclusions: The study highlights some uncertainty in the fraction of T1 relative to CS, influenced by inconsistent approaches in determining both boundaries. However, our findings serve as a foundation for remote analysis and prescription of exercise intensity, although testing is recommended for more precise applications. [ABSTRACT FROM AUTHOR]

This letter to the editor responds to a paper that compares different mathematical models for describing the relationship between exercise intensity and duration before task failure. The authors argue that a power law model is a better fit for a wide range of intensities and durations compared to the commonly used hyperbolic model. However, they also acknowledge that the hyperbolic model has physiological significance, particularly in terms of the critical power (CP) and work prime (W') parameters. The authors suggest that incorporating durability as a parameter may improve the accuracy of mathematical models for endurance performance. Overall, the letter emphasizes the importance of considering the physiological significance of parameters when choosing a mathematical model. [Extracted from the article]

Purpose: Altered movement complexity, indicative of system dysfunction, has been demonstrated with increased running velocity and neuromuscular fatigue. The critical velocity (CV) denotes a metabolic and neuromuscular fatigue threshold. It remains unclear whether changes to complexity during running are coupled with the exercise intensity domain in which it is performed. The purpose of this study was to examine whether movement variability and complexity differ exclusively above the CV intensity during running. Methods: Ten endurance-trained participants ran at 95%, 100%, 105% and 115% CV for 20 min or to task failure, whichever occurred first. Movement at the hip, knee, and ankle were sampled throughout using 3D motion analysis. Complexity of kinematics in the first and last 30 s were quantified using sample entropy (SampEn) and detrended fluctuation analysis (DFA-α). Variability was determined using standard deviation (SD). Results: SampEn decreased during all trials in knee flexion/extension and it increased in hip internal/external rotation, whilst DFA-α increased in knee internal/external rotation. SD of ankle plantar/dorsiflexion and inversion/eversion, knee internal/external rotation, and hip flexion/extension and abduction/adduction increased during trials. Hip flexion/extension SampEn values were lowest below CV. DFA-α was lower at higher velocities compared to velocities below CV in ankle plantar/dorsiflexion, hip flexion/extension, hip adduction/abduction, hip internal/external rotation. In hip flexion/extension SD was highest at 115% CV. Conclusions: Changes to kinematic complexity over time are consistent between heavy and severe intensity domains. The findings suggest running above CV results in increased movement complexity and variability, particularly at the hip, during treadmill running. [ABSTRACT FROM AUTHOR]