9th Mostindien Marathon

The 9th Mostindien Marathon took place on November 1. The half marathon, marathon, and 50 km ultramarathon were run in mild autumn weather.

Physiological and biomechanical aspects of the first female finisher in the longest triathlon in the world – Triple Deca in Ultra Triathlon Italy 2024

Pacing in multi-day long-distance triathlons has been investigated mainly in male athletes. We analyze physiological aspects such as energy expenditure and heart rate changes as well as biomechanical aspects in swimming (e.g. strokes per lane) and running (e.g. stride frequency, stride length, vertical ratio, vertical movement, ground contact time) in the first and only female triathlete to finish 30 IRONMAN®-distance triathlons in 30 days. The split times, lap times for swimming, cycling and running and variables were recorded with Fenix 7 Sapphire Solar with Normalized Power® (NP®), Intensity Factor® (IF®) and Training Stress Score® (TSS®), and were analyzed. The models’ estimations for pacing were assessed with R2. Variance (ANOVA) and associative (Pearson and Spearmen) analysis were conducted at a level of significance of 5 %. Swimming pace remained stable throughout the race (linear p = 0.473), cycling pace demonstrated a significant slowdown (third-order polynomial p < 0.001), and running pace significantly improved (third-order polynomial p < 0.001). Energy expenditure slightly decreased in swimming (p = 0.099) and progressively increased for both cycling (p = 0.034) and running (p = 0.044). Moderate-intensity swimming time initially increased and later decreased, with an opposite trend for high-intensity swimming time. Cycling times at both moderate and high intensities slightly decreased. Running showed decreasing moderate-intensity time and increasing high-intensity time, consistent with improved pace. Transition times increased over the race period, with T1 increasing more prominently. Biomechanical parameters in swimming, including total stroke count and SWOLF index, showed increasing trends. Overall, significant differences were observed in running time at moderate intensity (p < 0.001, η2 = 0.513), high intensity (p < 0.001, η2 = 0.518) and average pace (p < 0.001, η2 = 0.603). The athlete spent significantly more time at moderate intensity (p = 0.019 and p = 0.002) and significantly less time at high intensity (p = 0.011 and p = 0.005) running in the initial phase, compared to the middle and final stages of the race. All biomechanical variables decreased slightly in the opening phase of the race but then increased in the middle and final stages of the race. Overall, the results highlight that running was the discipline most affected by physiological and pacing adaptations throughout the race; while cycling and swimming parameters demonstrated weaker or no consistent associations.

7 Weird Things That Happen to Your Body When You Run a Marathon

You’ll sweat, cramp, cry, maybe even pee blood! Here’s just how far your body will go to get you across that finish line.

For more details, see the article of Richard A. Lovett

East-European runners were the fastest in 6-day ultramarathons

Ultramarathon running is increasingly popular, where the time-limited races offer formats from 6 hours to 10 days. To date, the origin of the best 6-day ultramarathoners and where the fastest races are held are yet to be determined. The aim of the present study was to investigate where these runners originate from, and where the fastest races are held. A total of 8,889 race records (6,737 from men and 2,162 from women) from 3,226 unique age group runners (2,413 men and 813 women) from 54 countries from age groups 18 to 75 years and participating in 141 races held in 25 different countries between 1874 and 2022 were analysed. A machine learning model based on the XGBoost Regression algorithm was built to predict running speed based on the athlete’s age, sex, country of origin, and where the race occurs. Model explainability tools were then used to investigate how each independent variable would influence the predicted running speed. Most athletes (62.5%) were from the USA, France, South Africa, Australia, Germany, and the UK. Almost 60% of the 6-day races took place in the USA and France. Athletes from Lithuania, Slovenia, and Namibia were the fastest. Ukraine holds the fastest 6-day races, ahead of Austria and Australia. The model rated the country where the race takes place as the most important predictor. Men were ~0.4 km/h faster than women except for the 75 years age group. The fastest runners were in the 35–39 years age group. East-European runners from Lithuania and Slovenia were the fastest in 6-day ultramarathons, where most of the races took place in the USA and France. The fastest 6-day races were in Ukraine, where the races were held as track races.

Pacing in New York City Marathon: a tracking approach

Most studies investigated the pacing strategy in marathon running races considering intervals of 5 km (i.e., ~3.1 miles). However, a pacing analysis considering shorter intervals (e.g. 1 mile) might provide more insights to understand marathon pacing strategy. The present study analyzed the tracking of pacing in marathon runners considering one-mile intervals from the 4th to the 26th race mile. The data comprised male and female finishers competing in the ‘New York City Marathon’ from 2011 to 2019. Available information included the overall ranking position, average running speed, and ranking position by age, race time, year of competition, running pace, and split times per mile from the 4th (first point of measurement) to the 26th mile (race finish). Male runners were split into three groups based on their final classification (1st–3rd position; 4th–10th position; higher than 10th position). The Kruskal–Wallis test was used to compare running speed among ranking position groups. Tracking was tested through autocorrelations. The confidence interval was fixed at 95%. For males, 14%, 13%, and 5% of the performance in the last mile was explained by running pace in mile 4, for the three groups, respectively. For women, about 60% of the pace variance in mile 24 was explained by the pace in mile 4. Autocorrelation results showed strong stability in running pace along the race (0.90, 0.91, and 0.76 for 1st–3rd position, 4th–10th position, and >10th position, respectively). Among finishers of the ‘New York City Marathon’ competing from 2011 to 2019, the performance in the last mile was correlated with the performance in the first mile studied (4th mile), regardless of ranking position. Marathon organizers could employ data-driven strategies to monitor and support runners throughout the race.

Freestyle master’s swimming: Nationality, sex, and performance trends in World Aquatics competitions (1986–2024)

In sports science, freestyle swimming has been thoroughly studied for particular performance-related factors. Nonetheless, it is unknown what countries the top freestyle swimmers are from, especially not for age group swimmers. In addition, the existing research on the performance of master freestyle swimmers has yet to confirm that male swimmers achieve faster times than their female counterparts across all age groups and distances. The current study looked into the nationalities and sexes of the top freestyle swimmers in each age group in World Aquatics competitions for the 50m, 100m, 200m, 400m, and 800m events from 1986 to 2024. The data (derived from (www.worldaquatics.com/masters/archives/masters-archives) were presented using mean, standard deviation, maximum and minimum values, and/or confidence intervals. The year of competition, age, age group, stroke, distance, and first and last names of each swimmer were noted. The nations were then divided into six groups: one group comprising all other countries and the top five nations with the greatest number of appearances in the top 10 fastest freestyle swimming times by distance each year. In freestyle swimming, most swimmers (30.6%) competed in the 50m event (n = 25,094, 10,909 female and 14,185 male), followed by the 100m event (25.6%, n = 20,961, 8,796 female and 12,165 male), the 200m event (17.4%, n = 14,309, 6,729 female and 7,580 male), the 400m event (13.4%, n = 10,956, 5,363 female and 5,593 male), and the 800m event (12.6%, n = 10,317, 5,179 female and 5,138 male). The results from the generalized linear models indicate that sex, age group, and the interaction between sex and age group all had significant effects on the 50m, 100m, 200m, 400m, and 800m races. Specifically, for the 50m races, the effect of sex was significant (x2 (1) = 3451.941, p < 0.001), as was the effect of age group (χ2 (13) = 19,295.169, p < 0.001), and the interaction between sex and age group (χ 2 (13) = 654.671, p < 0.001). The USA demonstrates quantitative dominance by contributing the greatest number of top 10 performers. Additionally, the study highlights significant sex-based performance differences, with males generally outperforming females in all age categories. This study comprehensively analyzes the performance trends observed in freestyle master swimming for nearly four decades.

An analysis of the 6-h ultra-marathon race using a machine learning approach

Ultra-marathon running popularity is increasing, with the 6-h run being the shortest time-limited ultra-marathon. Since very little is known regarding the country from which the fastest 6-h runners originate, the fastest age group, and where the fastest 6-h race courses are located, this study aims to close this gap. A machine learning model based on the XG Boost algorithm was built to predict running speed based on the athletes age, gender, country of origin, and the country where the race takes place. Model explainability tools were used to investigate how each independent variable would influence the predicted running speed. To assess the impact of individual performance against the other variables under study, a Mixed Effects Linear Model was also built. A total of 117,882 race records from 51,018 unique runners from 65 countries participating in races held in 56 different countries were analyzed. Participation is spread across a wide range of countries, with a high correlation between the country of origin and the country of the event. Most runners originated from Germany, Italy, France, the USA, and Sweden, with Europe (Belgium, Russia, Spain, Poland, Romania, and Lithuania), being the fastest. Most athletes competed in Italy, Germany, France, the USA, and The Netherlands. The fastest average running speeds were also achieved in European countries (Russia, Belgium, Poland, Netherlands, Romania, Croatia, and Lithuania). For athletes competing in a 6-h ultramarathon, gender was the most important predictor, followed by the origin of the athlete, the age, and the race location. The 6-h running event seems to be dominated by European athletes regarding both participation and performance.

Triathlon

Previous scientific work on triathlon has resulted in a chapter in the book Encyclopedia of Exercise Medicine in Health and Disease.

The chapter can be found at Triathlon | SpringerLink, and the entire book at Encyclopedia of Exercise Medicine in Health and Disease | SpringerLink.

Sex differences in performance and pacing in the greatest Quintuple Iron ultra-triathlon race in history: The IUTA World Championship 2024 in France

Pacing in ultra-triathlon has been investigated by analyzing lap times from Double to Deca Iron ultra-triathlon for World Cup races but not for a World Championship. The present study aimed to investigate pacing in ultra-triathletes competing in the fastest and largest World Championship in Quintuple Iron ultra-triathlon ever held in history. A total of 11 female and 24 male finishers who completed the 2024 Quintuple Ultra Triathlon World Championship in Colmar, France, were analyzed. Independent t-tests assessed sex-based performance variations with effect sizes (Cohen’s d). A two-way ANOVA evaluated the effects of sex and performance quartiles on cycling and running, with eta squared (η²) used to measure effect sizes. Overall, men were slower in swimming and cycling and faster in running and overall race time. The variability in lap times was similar in cycling for both women and men but higher in running for women. There was a significant interaction between sex and performance quartiles in cycling but not running. For cycling, the variability in performance was higher in men compared to women; for running, it was similar for both women and men. The finding that women outperformed men in swimming and cycling, likely due to the elite nature of the World Championship, which featured a highly selected and committed female cohort with a high completion rate. While both sexes showed consistent pacing in cycling, women exhibited greater variability in running, possibly due to more frequent breaks.