ECSS Paris 2023: OP-AP42
INTRODUCTION: While the short duration and high-intensity nature of sprint events hinder direct measurement of metabolic demands, a robust bioenergetic model can illuminate the interplay among aerobic, anaerobic lactic, and anaerobic alactic pathways. Although existing bioenergetic approaches indirectly capture sprinting’s energy contributions, they often underestimate the anaerobic alactic component. For instance, di Prampero et al. [1,2] noted that early acceleration phases present high metabolic power peaks, likely driven by alactic energy expenditure. Building on these insights, the aim of the present study is to propose a comprehensive bioenergetic model of sprint running, detailing the time course of aerobic, anaerobic lactic, and anaerobic alactic contributions across 100–400 m distances. METHODS: Velocity data from the 2009 World Athletics Championships [3] were used to estimate the metabolic power of men’s and women’s winning 100, 200, and 400 m performances, following the approach by di Prampero et al. [1,2]. Aerobic, anaerobic lactic and alactic power contributions were each represented by distinct mathematical functions: exponentially rising (aerobic), bi-exponential (anaerobic lactic) and log-normal (anaerobic alactic) functions. From these fits, total anaerobic lactic and alactic energy expenditures were derived, enabling the calculation of maximal anaerobic capacities for male and female athletes. Model validation involved simulating each race distance and comparing the simulated performances to actual results [3]. RESULTS: The model accurately predicted metabolic power trajectories for all sprint distances, with R2 ranging between 0.94 and 0.98. Estimated anaerobic alactic capacities were 376 J/kg (men) and 259 J/kg (women), while anaerobic lactic capacities were 1314 J/kg (men) and 1194 J/kg (women), consistent with theoretical expectations [2]. Anaerobic metabolism accounted for ~95%, 88%, and 70% of total energy in the men’s 100, 200, and 400 m events, and ~93%, 86%, and 69% for the women’s events. Mean percentage differences between actual and simulated distances was 0.31% (men) and 1.05% (women), with higher discrepancy in the women’s results potentially reflecting an underestimation of anaerobic lactic contributions. CONCLUSION: This bioenergetic model provides a detailed account of aerobic and anaerobic energy contributions during sprinting, aligning closely with theoretical and empirical data. Its framework offers insights into sprint bioenergetics, informing future modelling efforts and performance optimization. Further investigation across a broader range of distances is warranted to refine model functions and parameter estimates. 1. di Prampero et al., 2005; 2. di Prampero & Osgnach, 2018; 3. Graubner & Nixdorf, 2011
Read CV Jeremy BriandECSS Paris 2023: OP-AP42
INTRODUCTION: Identifying the smallest worthwhile performance improvement is essential for coaches and researchers to assess the impact of training strategies and tapering in elite sports. This improvement is typically defined as 0.3 times the standard deviation of an athlete’s race-to-race performance variability, which corresponds to gaining one additional medal every 10 competitions for those already competing at the highest level. However, since performance variability varies considerably across sports (ranging from 0.3% to 7.0%), sport-specific assessments are necessary to determine meaningful improvements. Therefore, this study aimed to estimate the variability between- and within- athletes, as well as the predictability of performance times across competitions in elite trail running (1) METHODS: For the purpose of this study, we considered elite male athletes with an ITRA score of 825 or higher (n = 941). We collected race results from the main international trail running events, including the Golden Trail World Series, Skyrunning World Series, World Mountain Running Association, major championships (World and European), and key ultratrail races from the 2024 season (56 races). A MATLAB script matched each athlete with his race times, and races were classified into ITRA categories: uphill-only (UP), XS, S, M-L, and XL-XXL (based on length and elevation profile). Between-athlete variability was expressed as the standard deviation (SD) of the coefficient of variation (CV) across athletes, while within-athlete variability was assessed using CV and SD of performance times. Performance predictability was evaluated with the intraclass correlation coefficient (ICC), with uncertainty expressed as 90% confidence limits. All analyses were performed using Hopkin’s spreadsheet (2) RESULTS: Between-athlete variability ranged from 5.43% in the UP category to 8.49% in XL-XXL, with XS (5.96%), S (7.24%), and M-L (6.66%) in between. Within-athlete variability was lower, with values ranging from 3.18% in XS to 5.34% in S, and 3.98%, 4.46%, and 4.32% for XS, M-L, and XL-XXL, respectively. ICC for performance predictability were moderate for UP (0.46) and S (0.45), and very high for XS (0.71) and XL-XXL (0.73), with M-L showing a high ICC (0.55). CONCLUSION: This study provides a detailed analysis of performance variability and predictability in elite trail running. Between-athlete variability was highest in the XL-XXL category, while within-athlete variability was relatively lower across all categories. Performance predictability was moderate to high across most categories, with the highest ICC values observed in the XS and XL-XXL groups. These findings highlight the importance of sport-specific assessments for evaluating performance improvements and understanding the variability in elite trail running. Coaches and researchers can use these insights to tailor better training and tapering strategies for athletes at the highest competitive levels. 1. Skattebo & Losnegard, 2018 2. Hopkins, 2015
Read CV Simone BettegaECSS Paris 2023: OP-AP42
INTRODUCTION: Pacing profiles play a critical role in race walking performance. This study aims to analyze the differences in pacing profiles, speed control capabilities, and the correlation between the ability to reproduce personal best performances and race results among female 20km race walkers of different competitive levels at the 2024 Paris Olympics. METHODS: This study utilized competition data from 43 female athletes who completed the 20km race walk at the 2024 Paris Olympics. Data were sourced from the official websites of the Paris Olympics and World Athletics. A quasi-experimental design was adopted, with athletes divided into four groups based on their final rankings (Group A: 1st–11th; Group B: 12th–22nd; Group C: 23rd–32nd; Group D: 33rd–43rd). Key variables included split times for each 1km segment, average speed per 5km, starting speed (average speed over the first 2km), peak speed, and the coefficient of speed variation. When data exhibited a normal distribution, analysis of variance (ANOVA), Pearson correlation analysis, and independent samples t-tests were employed to explore differences in pacing profiles, starting speed, peak speed, and the ability to reproduce personal best performances among the groups. RESULTS: ANOVA results for 5km split speeds across groups revealed significant differences (p < 0.05), with the disparities becoming more pronounced as the race progressed. Athletes in Group A exhibited significantly higher peak speeds compared to other groups (p < 0.05). While the starting speeds of Group A and Group B athletes showed no significant difference (p > 0.05), Group As starting speed was significantly higher than that of Groups C and D (p < 0.05). The time at which athletes fell below their starting speed varied across groups, with Group A maintaining their starting speed longer than other groups. The race results at the Paris Olympics showed a significant positive correlation with peak speed (r = 0.805, p < 0.001) and starting speed (r = 0.880, p < 0.001). Additionally, the ability to reproduce personal best performances was positively correlated with final Olympic rankings (r = 0.518, p < 0.01). CONCLUSION: Athletes in different groups demonstrated distinct pacing profiles. Group A athletes primarily adopted a negative-split strategy, maintaining a conservative pace in the first half of the race, gradually increasing speed, and finishing with a strong sprint. In contrast, athletes in Groups B, C, and D tended to adopt a positive pacing strategy, reaching their peak speed earlier and subsequently slowing down. Higher-ranked athletes exhibited superior speed control, characterized by smaller variations in starting speed, peak speed, and split speeds, later occurrence of peak speed, and a delayed drop below starting speed, which contributed to their stronger finishing capabilities. These athletes were also more capable of delivering performances close to their personal bests in the Olympic setting.4
Read CV yue duanECSS Paris 2023: OP-AP42