ECSS Paris 2023: OP-AP45
INTRODUCTION: Typical training load data for age-group triathletes has not been established based on long-term, objective data captured by technology. Establishing the typical TL of age-group athletes from this data may provide opportunities to improve the health and performance outcomes of athletes. Training load quantified through metrics like duration, distance, heart rate (HR), and Training Stress Score (TSS) – a load metric based on Banister’s Training Impulse (TRIMP) [2], must be managed effectively to ensure performance optimisation and injury prevention [3]. Existing studies on TL are limited by reliance on subjective recall data [4], small sample sizes [5], or lack of comparison across factors like sex, age, race distance preference, and training phases. These comparisons are important to determine whether coaches may need to adjust the prescribed load across athlete categories. METHODS: This retrospective cohort study analysed six months of objective training data exported by 95 age-group triathletes (18 female, 77 male) from the TrainingPeaks® training management system. Load metrics included weekly duration, distance, HR, and TSS. Data from 34,731 training sessions were coded for sex, age group, race distance preference, and training phase (General, Specific, Taper/Race/Post, Off-Season). A Generalised Linear Model (GLM) determined whether sex, age, race distance preference, and training phase affected load metrics. RESULTS: Race distance preference and training phase showed significant differences between TL metrics (p = < 0.05), while sex and age did not. Long-course athletes had higher mean weekly TL (574 TSS, 10.25 hrs, 171 km) compared to short-course athletes (452 TSS, 8.45 hrs., 118 km). TL for all phases were significantly different (p < 0.05). The Specific phase showed the highest mean TL (620 TSS, 9.27 hrs, 166 km), while the Off-Season was the lowest (364 TSS, 5.67 hrs, 84 km). CONCLUSION: Male and female athletes had similar TLs, as did athletes of different ages. Long-course athletes and those in the Specific training phase had the highest TL. Coaches and athletes can use these typical age-group loads to compare and adjust absolute TL according to the magnitude of change between training phases and race distance preference. Further research is needed to examine how variations in TL impact performance and injury risk, helping to refine TL guidelines. This study provides the first large-scale, objective TL dataset indicating typical load for age-group triathletes, offering actionable insights for training personalisation. References 1. Knechtle, et al (2014) 2. Banister, et al (1999) 3. Etxebarria, et al (2019) 4. Sinisgalli, et al (2021) 5. Falk Neto, et al (2021)
Read CV Leighton WellsECSS Paris 2023: OP-AP45
INTRODUCTION: The objective of these four case studies is to describe the individual training characteristics of world-class male triathletes in the year leading up to the Olympics. Specifically, the aim is to determine whether periodization and intensity distribution follow common patterns or vary significantly based on individual needs and physiological characteristics. METHODS: Training & physiological data from four world-class male triathletes (27 ± 4 years, VO₂max: 75 ± 2 ml·min⁻¹·kg⁻¹) were analyzed. Two exhibited predominantly glycolytic characteristics, with a lower %VO₂max at the anaerobic threshold (83.7 ± 0.3%), while the other two displayed predominantly oxidative characteristics, with a higher %VO₂max (86.1 ± 0.2%). Day-to-day power meter, GPS, and chronometer data from a 35-week macrocycle were retrospectively analyzed. Training volume, intensity distribution, altitude training, hypoxic exposure, competition schedule, and tapering strategies were considered. Training data were divided into two periods (General and Specific), and the three disciplines (swimming, cycling, running) were analyzed by total time spent in three physiological zones (Z1, Z2, Z3). RESULTS: Athletes trained 676 ± 22 hours across 610 ± 34 sessions in the final macrocycle before the Olympics, with 87,0% of training below the first lactate threshold (Z1). Intensity distribution shifted from 86.7%, 8.3%, and 4.9% in the general period to 87.3%, 6.9%, and 5.8% in the specific period for Z1, Z2, and Z3, respectively. The polarized index increased from 1.72 to 1.82, indicating a greater polarization closer to the event. More glycolytic athletes exhibited a higher polarized index than oxidative athletes in both periods (1.79 ± 0.42 vs. 1.65 ± 0.00; 1.92 ± 0.11 vs. 1.81 ± 0.09). All four athletes trained at altitude for 28, 42, 23, and 25 days; two used a hypoxic tent for 21 and 25 days. Despite different pre-taper strategies, all athletes reduced training volume by 23.3 ± 8.4% in the week before Olympics without altering frequency or intensity. VO₂max increased by 2.3% from the general to the specific period, while the second lactate threshold improved by 1.4% (swimming), 5.2% (cycling), and 2.7% (running). Notably, two athletes reached the Olympic podium. Moreover, those who competed in fewer races during the Olympic year performed better at the Games. CONCLUSION: This study aligns with best practices in the literature regarding training volume and intensity distribution in Olympic-level triathletes. Achieving Olympic qualification and peak performance requires high training volumes, predominantly at low intensity, regardless of physiological characteristics. The most common strategy involves increasing intensity from the general to the specific period, followed by a short taper before competition. However, these best practices must be adapted to individual athlete characteristics. Furthermore, fewer races in the Olympic year appear associated with better Olympic performance.
Read CV Lorenzo BudelECSS Paris 2023: OP-AP45
INTRODUCTION: The Valencia Marathon is a race with the platinum distinction from World Athletics (WA), with a record of 2:01:48 as the best marathon in Spain according to the Royal Spanish Athletics Federation (RFEA). There are various biomechanical variables that play a very important role in both running economy and athletic performance of the runners. Among these variables are the foot strike angle (FSA), duty factor (DF), ground contact time, flight time, and running cadence (1). Knowing these variables in the context of an official competition allows us to understand the reality of the athletes performance (2). The objective of this study is to analyse the foot strike pattern and spatiotemporal variables that describe the running technique of elite marathon runners in a real competition context METHODS: A biomechanical study of running in the sagittal plane (240Hz) was conducted on the top 48 athletes of the 2021 Valencia Marathon, with times ranging from 2:05:12 to 2:15:59. The sample was divided into two groups: from the 1st to the 24th place (G1) and from the 25th to the 48th place (G2). Two independent observers analysed variables such as: FSA (3), DF (4), running cadence, flexion time, and impulse time. To determine the differences between groups, the Students t-test for independent samples was used for parametric variables, and the Mann-Whitney U test was used for non-parametric variables (p<0.05). RESULTS: Regarding the FSA, it was observed that 61.5% of the analysed athletes run with a midfoot strike (MFS), 33.3% with a rearfoot strike pattern (RFS), and only 5.2% with a forefoot strike pattern (FFS). No differences were found between groups in terms of foot strike pattern (p>0.05). On the other hand, differences were observed between groups in variables such as contact time (G1: 174ms ±7, G2: 183ms±12, P>0.001), flexion time (G1: 78ms ±6, G2: 81ms±7, P>0.05), impulse time (G1: 96ms ±8, G2: 103ms±8, P>0.05), cadence (G1: 186 spm ±6, G2: 181 spm±7, P>0.05). However, no differences were found in DF (G1: 37%±3, G2: 38%±2, P>0.05). CONCLUSION: This study has allowed us to characterize the running technique of elite marathon runners in a real context. Regarding the foot strike technique, it was found that most runners were MFS, results similar to those obtained in other recent studies (2) and contrary to previous studies (5). On the other hand, in a very homogeneous sample of runners, differences were observed in various spatiotemporal variables such as cadence or ground contact time. This contrasts with the equality observed in DF. These results can help coaches understand how their athletes perform relative to their peers and make informed decisions for training planning. REFERENCES: 1) Folland et al., Medicine and science in sports and exercise, 2017 2) Gamez-Paya et al., Appl Science, 2023 3) Hasegawa et al., JstregthCondRes, 2007 4) Gray et al., JstregthCondRes, 2019 5) Hanley et al., Jbiomech, 2019
Read CV JAVIER Gámez-PayáECSS Paris 2023: OP-AP45