RPE

Introduction Lactate has been redefined from a metabolic waste product to a key signaling molecule regulating energy metabolism, gene expression, and disease progression, making the precise regulation of exercise-induced lactate exposure critical for population-specific prescriptions (e.g., for the elderly or cancer patients). The aim of this study was to investigate the coupling between lactate accumulation and subjective fatigue by comparing whole-body (running) versus localized (cycling) exercise, as well as intermittent versus continuous modalities. Methods Twelve healthy adults (six males and six females) participated in this study, completing combinations of three intensities and two modalities: Moderate-Intensity Interval Training (MIIT), Moderate-Intensity Continuous Training (MICT), and High-Intensity Interval Training (HIIT) via running and cycling ergometry. Blood lactate area-under-curve (AUC) was calculated, and Lactate Production Efficiency (LPE = AUC/RPE) was innovatively proposed to quantify lactate exposure per unit of Rating of Perceived Exertion (RPE). Results Cycling induced 59% (MIIT, p < 0.01) and 67% (HIIT, p < 0.05) higher lactate AUC than running, irrespective of intensity or intermittency. Furthermore, HIIT cycling yielded a 52% higher LPE than running (20.48 vs. 13.47 mmol·min/L·scale, p < 0.01), indicating superior lactate stress per fatigue unit and optimizing lactate elevation, whereas running MIIT minimized lactate exposure. Males showed 36–43% higher running AUC than females (p < 0.05), suggesting heightened metabolic sensitivity. Interval efficacy was also confirmed, as HIIT increased lactate AUC by 44–87% versus MICT (p < 0.05). Discussion The introduction of LPE effectively quantifies exercise-modality effects on fatigue-lactate decoupling. The findings demonstrate metabolic stress concentration in localized exercise (cycling) and male-specific lactate sensitivity during whole-body running, providing RPE-based strategies for precision exercise prescription and advancing personalized interventions in sports medicine.

matched text: “rpe”

This study examined the relationships between fatigue-related blood biomarkers, training load distribution, and performance changes in elite triathletes across a structured training season. Twenty athletes (11 international-level and 9 national-level) were monitored. Exercise stimuli were quantified using objective and subjective metrics during the general and specific preparatory periods, with each microcycle classified as heavy, moderate, or low according to accumulated weekly load. Baseline and follow-up blood samples were collected to assess urea and creatine kinase (CK), and an incremental cycling test with gas analysis was performed at the beginning and end of the training period to evaluate changes in physiological performance. Statistical analyses assessed pre–post differences, while correlation analyses explored associations between training load, biomarker responses, and performance outcomes. Urea concentrations increased significantly during severe (p < 0.001; ES: 2.1) and moderate (p < 0.001; ES: 1.2) load microcycles compared with baseline, whereas CK showed no meaningful associations across the season. Greater accumulated subjective training load was associated with larger increases in urea (ϱ = 0.67; p < 0.05), whereas a more favorable balance between the objectively monitored training load and the athlete’s perceived exertion—characterized by lower perceived effort relative to the external load—was associated with smaller urea elevations (ϱ = -0.55; p<0.05). In addition, improvements in relative power at V̇O 2 max and VT2 were negatively associated with increases in urea ((ϱ = -0.65; p < 0.05) (ϱ = -0.68; p < 0.05) respectively). Despite phases of comparable exercise stimuli, no differences in biomarker responses were observed between national- and international-level triathletes. These findings suggest that longitudinal monitoring of urea may provide useful information to help identify potential states of excessive training load in athletes. However, the substantial inter-individual variability observed indicates that isolated biomarker measurements are insufficient to identify non-adaptive responses. Therefore, an individualized, longitudinal, and holistic monitoring framework integrating objective and subjective training load measures is required to better understand training-induced adaptations and fatigue.

matched text: “perceived exertion”