Fran Oficial-Casado

Background: Cycling performance depends on both aerobic capacity and neuromuscular function, with recent training approaches emphasizing the role of strength training. However, the extent to which neuromuscular characteristics assessed in conventional strength exercises transfer to cycling performance remains unclear. Therefore, the aim of this study was to analyze the relationship between the Load–Velocity (L-V) profile obtained from a multi-joint strength exercise (half squat) and the cycling Power Profile (PP) in Masters-level cyclists. Methods: Twelve masters-level cyclists were evaluated by the L-V and the PP test. The cycling PP was determined through maximal efforts of 1, 5, and 20 min, expressed relative to body mass (W·kg⁻¹). The L-V profile was assessed during the half squat using a progressive loading protocol with load–velocity monitoring. Pearson’s correlation analyses were performed between the slope and intercept of the L-V profile relationship and PP variables, as well as mean ascent velocity (VAM). Results: No significant relationships were observed between L-V profile variables and cycling performance (r = −0.21 to 0.09, p > 0.05). In contrast, VAM showed very large associations with P1 (r = 0.81, p = 0.001) and P5 (r = 0.86, p < 0.001). The regression model explained a large proportion of the variance in VAM (R² = 0.75, p = 0.01). Conclusions: Strength performance assessed through a conventional exercise such as the half squat is not directly related to cycling PP in masters-level cyclists. The observed relationships between VAM and cycling PP reinforce the importance of task specificity.

Although the Collaborative Online International Learning (COIL) methodology is expanding, its use in physiology is still scarce. Our example COIL project of 7 weeks developed a sports-assessment service concept focused on physiology and biomechanics assessments. The program involved teams from Brazil, Germany, and Spain, comprising undergraduate and master’s students. Students perceived extracurricular activities in this format as beneficial. Coaches also expressed positive views about such initiatives, noting benefits for students and their development.

Objective . Threshold determination for improving training and sports performance is important for researchers and trainers, who currently use different methods for determining lactate, ventilatory or muscle oxygenation (SmO ₂ ) thresholds. Our study aimed to compare the identification of the intensity at the first and second thresholds using lactate and SmO ₂ data by different mathematical methods in different muscles during a graded cycling test. Approach . Twenty-six cyclists (15 males and 11 females; 23 ±6 years, 1.71 ± 0.09 m, 64.3 ± 8.8 Kg and 12 ± 3 training hours per week) performed a graded test on the cycle ergometer. Power output and saturation of muscle oxygen in four muscles (vastus lateralis, biceps femoris, gastrocnemius and tibialis anterior) were measured, along with systemic lactate concentration. Main Results . Our results showed that any method was reliable for determining the first muscle oxygenation threshold (MOT1) when comparing the lactate threshold in any muscle. However, the best method for determining the second muscle oxygenation threshold (MOT2) was the Exp-Dmax ( p < 0.01; ICC = 0.79–0.91) in all muscles. In particular, the vastus lateralis muscle showed the highest intraclass correlation coefficient (ICC = 0.91, CI95% [0.81, 0.96]). However, results varied per sex across all muscles analyzed. Significance . Although the first muscle oxygenation threshold could not be determined using mathematical methods in all the muscles analyzed, the Exp-Dmax method presented excellent results in detecting the second systemic threshold in the vastus lateralis.

Running pacing has become a focus of interest over recent years due to its relationship with performance, however, it is still unknown the consistency of each race in different editions. The aim of this study is to analyze the consistency of pacing profile in three consecutive editions of three marathon races. A database of 282,808 runners, compiled from three different races (Chicago, London, and Tokyo Marathon) and three editions (2017, 2018, and 2019) was analyzed. Participants were categorized according to their time performance in the marathon, every 30 min from 2:30 h to sub-6 h. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. The intraclass correlation coefficients (ICC) of relative speed at the different pacing section, taking into account the runner time categories, was excellent over the three marathon editions (ICC > 0.93). The artificial intelligence model showed an accuracy of 86.8% to classify the runners' data in three marathons, suggesting a consistency between editions with identifiable differences between races. In conclusion, although some differences have been observed between editions in certain sections and marathon runner categories, excellent consistency of the pacing profile was observed. The study of pacing profile in a specific marathon can, therefore, be helpful for runners, coaches and marathon organizers for planning the race and improving its organization.

This study aimed to analyse differences in pacing profiles in four marathon competitions and to explore that pacing per time category. A database of 91,493 runners gathered from 4 different races was analysed (Valencia, Chicago, London and Tokyo Marathon). Participants were categorized in accordance with their completion time. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. In the four marathons studied, the first 5 km differed widely, presenting London the highest relative speeds (5 km: CI95% London vs. Valencia [12.1, 13.6%], p < 0.001 and ES = 2.1; London vs. Chicago [5.5, 7.1%], p < 0.001 and ES = 1.1; London vs. Tokyo [15.2, 16.8%], p < 0.001 and ES = 2.3). Races did not differ at each section for high‐performance runners (sub‐2:30), but differences between races increased as the time category increases (e.g. 35 km and sub‐3:00: CI95% London vs. Tokyo [−3.1, −1.8%], p < 0.001 and ES = 0.7; 35 km and sub‐5:00: London vs. Tokyo [−9.8, −9.2%], p < 0.001 and ES = 1.3). The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons (e.g. CI95% London vs. Valencia [10.3, 10.8%], p < 0.001 and ES = 1.3). In conclusion, although race characteristics affect pacing, this effect was higher as the category time increases. Race pacing characteristics should be taken into consideration for runners and coaches choosing the race and working on pacing strategies, for researches to extrapolate or interpret results, or for race organizations to improve its pacing characteristics.

Objective : The objective of this study was to determine the association between skin temperature response and the physiological stress after a half marathon. Approach : Seventeen runners were measured 48 h before, 24 h before, 24 h after and 48 h after completing a half marathon. The measurements on each day of testing included blood markers (creatine kinase [CK] and glutamate oxaloacetate transaminase [GOT]), perception of pain and fatigue (using a visual analogue scale), skin temperature (using infrared thermography), and jump performance (using countermovement jump test). Main results : CK ( p < 0.001 and ES = 2.1), GOT ( p = 0.04 and ES = 1.3), and perception of fatigue and pain ( p < 0.001 and ES > 1.0) increased 24 h after the half marathon, whereas jump performance decreased ( p < 0.01 and ES = 0.4). No increase of skin temperature was observed in the tests after the competition and no regression model was able to predict physiological stress using skin temperature. Only a bivariate correlation was observed between the 24 h variation (pre—24 h) of CK and the skin temperature of the posterior upper limb ( p = 0.04 and r = 0.5), and between the 48 h variation (pre—48 h) of pain perceived and the skin temperature of the knee ( p < 0.01 and r = 0.6). Significance : In conclusion, follow-up on basal skin temperatures does not seem to be an adequate method to detect physiological stress after a half marathon. In line with the observed results, we recommend caution when interpreting peaks in basal skin temperature in field sports assessments.

Although the use of sports compression socks is becoming more popular, there is still a lack of knowledge regarding the helpfulness of these garments for physical movement or sports use. One of the main effects attributed to the use of compression sock is the facilitation of venous blood flow return which is thought to improve performance and recovery. In this study, 10 trained runners performed 2 treadmill running tests for 30 min to 75% of their maximum aerobic speed, without sports socks (control) and with different sports compression socks. We measured popliteal vein flow volume before and after each test using magnetic resonance imaging. No differences were observed between the conditions before the test (p > 0.05), but there were differences between the high compression socks condition and the control after the test (p < 0.05). Exercise increased the venous flow in both legs in the control (p < 0.001) as well as in the right leg with the high compression sock type (p < 0.01) but there were no differences in the behaviour of the different sock compression levels (p > 0.05). The use of sports compression socks with different levels of compression does not increase the volume of venous blood return via the popliteal vein in trained athletes after running for 30 min.