Some studies show interest in measuring heart rate variability (HRV) during post-exercise recovery. It is known that the parasympathetic system is relevant during this process, where one of the factors of this modulation is the interaction of acetylcholine and cholinesterases (ChE). However, the behavior of ChE and its relationship during recovery is little known; therefore, the objective of this study was to analyze the behavior of ChE and its relationship with recovery evaluated in HRV indicators in volleyball players. An exercise protocol with long-term and intermittent high-intensity phases was applied in nine volleyball players. HRV measurements were made, and blood samples were drawn to evaluate the ChE before exercise and after 24 and 48 h post-exercise. The results show a modification of the variables after exercises with respect to the baseline values (ChE: 1818.4 ± 588.75 to 2218.78 ± 1101.58; RMSSD: 42.64 ± 12.86 to 17.72 ± 12.55 (p < 0.05); SS: 8.76 ± 1.93 to 21.93 ± 10.05 (p < 0.01); S/PS Ratio: 0.32 ± 0.14 to 3.26 ± 3.28 (p < 0.01)), as well as recovery after 24 and 48 h with respect to postexercise (ChE: 1608.81 ± 546.88 (p < 0.05) and 1454.54 ± 580.45 (p < 0.01); RMSSD: 43.83 ± 24.50 and 46.18 ± 33.22 (p < 0.01); SS; 10.93 ± 5.16 and 11.86 ± 4.32 (p < 0.01); S/PS Ratio: 0.46 ± 0.32 and 0.50 ± 0.28 (p < 0.01)). ChE correlations (p < 0.001) were found with moderate (SS: r = 0.465) and large (RMSSD: r = −0.654; S/PS Ratio: r = 0.666) HRV indexes. In conclusion, ChE modifications are related to changes in HRV showing a very similar behavior in the case of the study subjects.

El objetivo del estudio fue determinar el perfil fuerza-velocidad en pitchers universitarios mediante la adaptación de este indicador a la mecánica de lanzamiento, utilizando pelotas de distintos pesos para evaluar la relación entre fuerza aplicada y velocidad generada para esto se realizó un estudio cuantitativo y transversal con (n=9) lanzadores masculinos (18–24 años). Se midió la velocidad (con radar Stalker Pro II) y la fuerza (F=m·a) al lanzar pelotas de 3, 5, 7 y 9 oz. Los datos se analizaron con estadística descriptiva (media ± DE) y se realizó una correlación de Pearson un análisis de varianza (ANOVA) y la prueba de Tukey. Se observó una relación inversa entre fuerza y velocidad: al aumentar el peso de la pelota, la velocidad disminuyó (de 79.0 ± 4.64 mph con 3 oz a 65.2 ± 2.97 mph con 9 oz), mientras que la fuerza aumentó (de 28.0 ± 14.35 N a 76.7 ± 32.58 N). Los perfiles individuales mostraron alta variabilidad (R² entre 0.6732 y 0.9623), destacando diferencias en la eficiencia neuromuscular entre lanzadores. La adaptación del perfil fuerza-velocidad a la mecánica de lanzamiento es viable y revela información clave para personalizar el entrenamiento. Los resultados sugieren que este método puede optimizar el rendimiento y prevenir lesiones al identificar desequilibrios específicos en cada pitcher.

Introduction: Internal load quantification constituted a fundamental element for training planning and injury prevention in team sports. Most basketball studies employed univariate approaches that failed to capture the multidimensional nature of load. Objective: Internal load in Mexican university basketball was analyzed using an integrated multivariate approach combining principal component analysis, linear mixed models, the Mahalanobis-Taguchi system and Bland-Altman concordance, evaluating differences by playing position, game period, and match outcome. Methodology: Seventeen male players from a university team participated, yielding 236 observations across 22 official games. Physiological load was quantified using the Training Impulse (Edwards' TRIMP) per game quarter and perceptual load with the Rating of Perceived Exertion scale. Principal component analysis, linear mixed models, the Mahalanobis-Taguchi system, and Bland-Altman concordance analysis were applied. Results: Principal component analysis revealed a single component explaining 55.28% of variance, evidencing unidimensionality of internal load. Linear mixed models identified game period as the dominant factor, with greater demand in the second and fourth quarters. In lost games, load did not decrease in the third quarter. Perceived exertion differed between positions while Training Impulse showed no differences, evidencing perceptual-physiological dissociation. Discussion: Results were consistent with prior studies reporting unidimensionality of internal load in team sports. The sawtooth temporal pattern replicated findings from elite basketball and the positional perceptual-physiological dissociation suggested selecting monitoring indicators according to playing position. Conclusions: The integrated multivariate approach reveals internal load patterns undetectable through conventional univariate analysis, with direct implications for individualized training load management.