No hyponatremia despite continuous plasma sodium decline in female runners during a seven stage ultramarathon

The role of sodium supplements and sex in the occurrence of exercise-associated hyponatremia (EAH) remains controversial. This study investigated hydration status in ultrarunners (19 males and 9 females) who completed seven marathons over seven consecutive days. Due to the limited number of female participants, no statistical comparison between sexes was performed. Plasma sodium concentration ([Na+]) and multiple hydration markers were assessed before, during, and after the race. Reported sodium supplement consumption showed no association with plasma [Na+]. An overall decline in plasma [Na+] was observed in females (regression slope = -1.278, p = 0.02) across the event, whereas no significant change was detected in males (slope = -0.325, p = 0.57). Additionally, no significant associations were found between plasma [Na+] and other monitored variables, including sodium supplement intake, pre-race hydration strategy, body mass, total body water, plasma osmolality, hematocrit, hemoglobin, urine specific gravity, urinary [Na+], thirst rating, or fluid intake reported pre-, during, and post-stage. No cases of symptomatic or asymptomatic hyponatremia were identified, suggesting that total fluid and sodium intake were adequate to maintain fluid-electrolyte balance and prevent EAH in both sexes.

Race course characteristics are the most important predictors in 48 h ultramarathon running

Ultra-marathon running - where races are held in distance-limited (50 km, 50 miles, 100 km, 100 miles, etc.), time-limited (6 h, 12 h, 24 h, 48 h, 72 h, etc.), and multi-stage races - is gaining in popularity. However, we have no knowledge of where the fastest 48-hour runners originate and where the fastest 48-hour races are held. This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). Model explainability tools were then used to investigate how each independent variable would influence the predicted result. A sample of 16,233 race records from 7,075 unique runners originating from 60 different countries and participating in races held in 36 different countries between 1980 and 2022 was analyzed. Participation was spread across many countries, with USA, France, Germany, and Australia at the top of the participants’ rankings. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. The XG Boost model showed that elevation of the course (flat course) and the running surface (track) were the variables that had a larger influence on the running speed. The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. Men were ~ 0.5 km/h faster than women. Most finishers were 45–49 years old, and runners in this age group achieved the fastest running speeds. In summary, elevation of the course (flat course) and the running surface (track) were the most important variables for fast 48-hour races, whilst the country of origin of the athlete and the country where the event was hold would lead to the broadest difference in the predicted running speed range. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. Any athlete intending to achieve a personal best performance in this race format can benefit from these findings by selecting the most appropriate race course.

Cycling and Running are More Predictive of Overall Race Finish Time than Swimming in IRONMAN® Age Group Triathletes

Several studies have evaluated the most predictive discipline (swimming, cycling, and running) of performance in elite IRONMAN® triathletes. However, no study has ever determined the most decisive discipline for IRONMAN® age group triathletes. The present study analyzed the importance of the three disciplines on the overall race times in IRONMAN® age group triathletes, in order to try and determine the most predictive discipline in IRONMAN® for age group triathletes, and whether the importance of the split disciplines changes with increasing age. This cross-sectional study used 687,696 IRONMAN® age group triathletes race records (553,608 from males and 134,088 from females). Age group athletes were divided in 5-year age groups (i.e., 18–24, 25–29, 30–34,…,70–74, and last 75 + years). The relationships between split disciplines (i.e., swimming, cycling, and running) and overall race times were evaluated using Spearman and Pearson correlations. A multi-linear regression model was used to calculate their prediction strength. The overall finish time correlated more with cycling and running times than with swimming times for both male and female IRONMAN® age group triathletes (r = 0.88 and r = 0.89 for females; r = 0.89 and r = 0.90 for males, respectively). All correlation coefficients decreased with increasing age, which was more noticeable for the swimming discipline. Both cycling and running are more predictive than swimming in IRONMAN® age group triathletes, where the correlation between the overall race times and the split times decreased with increasing age more in swimming than in cycling and running. These insights are useful for IRONMAN® age group triathletes and their coaches in planning their IRONMAN® race preparation and concentrating training on the more predictive disciplines.

Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model

Although the 50-mile ultramarathon is one of the most common race distances, it has received little scientific attention. The objective of this study was to assess how an athlete’s age group, sex, nationality, and the race location, affect race speed. Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. Model explainability tools, including model features relative importances and prediction distribution plots were then used to investigate how each feature affects the predicted race speed. The most important features, with respect to the predictive power of the XGBoost model, were the location of the race and the athlete’s gender. The top 3 countries with the fastest predicted median race speeds were Slovenia, New Zealand, and Bulgaria for nationality and New Zealand, Croatia, and Serbia for the race location. The fastest median race speed was predicted for the age group 20–24 years, but a marked age-related performance decline only became apparent from the age group 40–44 years onward. Model predictions for male athletes were faster than for female athletes. This study offers insights into factors influencing race speed in 50-mile ultramarathons, which may be beneficial for athletes, coaches, and race organizers. The identification of nationalities and event countries with fast race speeds provides a foundation for further exploration in the field of ultramarathon events.

Pacing in ultra-marathon running: the Western States 100-mile endurance run 2006–2023

Pacing has been investigated in different running races, including ultra-marathons. We have, however, little knowledge about pacing in ultra-trail running. To date, no study has investigated pacing in one of the most iconic ultra-trail running races, the ‘Western States 100-Mile Endurance Run’ (WSER), which covers 160 km (100 miles) and includes significant elevation changes (6000 vertical meters uphill and 7500 vertical meters downhill). Therefore, the aim of the study was to investigate pacing for successful finishers in WSER regarding gender, age, and performance level. Official results and split times for the WSER were obtained from the race website, including elevation data from 3837 runners, with 3068 men (80%) and 769 women (20%) competing between 2006 and 2023. The mean race speed was calculated for each participant, as well as the average mean checkpoint speed for each of the 18 race checkpoints (17 aid stations and finish point). The percentage of change in checkpoint speed (CCS) in relation to the average race speed was calculated. CCS was calculated for each of the 18 checkpoints to evaluate each runner’s pacing strategy. The average change in checkpoint speed (ACCS) of each participant was calculated as a mean of the 18 CCSs. Eight age groups were formed. Since there were very few runners younger than 25 and older than 65 years, these age groups were merged into < 30 and 60 > groups, respectively. Four performance groups were formed by four quartiles, each consisting of 25% of the total sample separately for men and women. Pacing shows great variability between checkpoints in both men and women, mainly influenced by elevation. Although the race profile is mostly downhill, it appears that the pacing trend is towards positive pacing. The differences between men and women were mainly at the beginning of the race (men start faster) and towards the end (men slow down more). Men have more pacing variability than women, with significant differences in the youngest age group, as well as the 40–44 and 50–54 age groups. In addition, younger men have more variability in pace compared to older men. There are no significant differences in age groups in women. Finally, the slowest and fastest ultra runners had less pacing variability than medium level runners. Pacing in WSER-runners shows great variability between checkpoints in both men and women. Pacing is positive and highly influenced by elevation. Men start faster than women, and men slow down more than women. Pacing differs in male but not in female age group runners. The slowest and fastest ultra runners had less pacing variability than medium level runners.

Pop-Up Mostindien Marathon

The season kicked off on February 22nd with the Pop-Up Most India Marathon, where the members of the 100 Marathon Club Switzerland were able to add another marathon to their account.

Christian Marti was on the hunt for marathon number 600 and Beat Knechtle for marathon 500.

Analysis of the 10-day ultra-marathon using a predictive XG boost model

Ultra-marathon running races are held as distance-limited or time-limited events, ranging from 6 h to 10 days. Only a few runners compete in 10-day events, and so far, we have little knowledge about the athletes’ origins, performance, and event characteristics. The aim of the present study was to investigate the origin and performance of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. The model explainability tools were then used to investigate how each independent variable would influence the predicted running speed. The model rated the origin of the athlete as the most important predictor, followed by age group, running on dirt path, gender, running on asphalt, and event location. Running on dirt path led to a significant reduction of running speed, while running on asphalt showed faster running speeds compared to other surfaces. Most athletes came from USA, followed by Russia, Germany, Ukraine, the Czech Republic, and Slovakia. Most of the runners competed in USA. The fastest 10-day runners were from Finland and Israel. The fastest 10-day races were held in Greece. Most 10-day runners originated from USA, but the fastest runners originate from Finland and Israel. The fastest race courses were in Greece. Running on dirt paths leads to a significant reduction in running speed while running on asphalt leads to faster running speeds.

The influence of origin and race location on performance in IRONMAN® age group triathletes

The IRONMAN® (IM) triathlon is a popular multi-sport, where age group athletes often strive to qualify for the IM World Championship in Hawaii. The aim of the present study was to investigate the location of the fastest IM racecourses for age group IM triathletes. This knowledge will help IM age group triathletes find the best racecourse, considering their strengths and weaknesses, to qualify. To determine the fastest IM racecourse for age group IM triathletes using descriptive and predictive statistical methods. We collected and analyzed 677,702 age group IM finishers’ records from 228 countries participating in 444 IM competitions held between 2002 and 2022 across 66 event locations. Locations were ranked by average race speed (performance), and countries were sorted by number of records in the sample (participation). A predictive model was built with race finish time as the predicted variable and the triathlete’s gender, age group, country of origin, event location, average air, and water temperatures in each location as predictors. The model was trained with 75% of the available data and was validated against the remaining 25%. Several model interpretability tools were used to explore how each predictor contributed to the model’s predictive power, from which we intended to infer whether one or more predictors were more important than the others. The average race speed ranking showed IM Vitoria-Gasteiz (1 race only), IM Copenhagen (8 races), IM Hawaii (18 races), IM Tallinn (4 races) and IM Regensburg (2 races) in the first five positions. The XG Boost Regressor model analysis indicated that the IM Hawaii course was the fastest race course and that male athletes aged 35 years and younger were the fastest. Most of the finishers were competing in IM triathlons held in the US, such as IM Wisconsin, IM Florida, IM Lake Placid, IM Arizona, and IM Hawaii, where the IM World Championship took place. However, the fastest average times were achieved in IM Vitoria-Gasteiz, IM Copenhagen, IM Hawaii, IM Tallin, IM Regensburg, IM Brazil Florianopolis, IM Barcelona, or IM Austria with the absolutely fastest race time in IM Hawaii. Most of the successful IM finishers originated from the US, followed by athletes from the UK, Canada, Australia, Germany, and France. The best mean IM race times were achieved by athletes from Austria, Germany, Belgium, Switzerland, Finland, and Denmark. Regarding environmental conditions, the best IM race times were achieved at an air temperature of ∼27°C and a water temperature of ∼24°C. IM age group athletes who intend to qualify for IM World Championship in IM Hawaii are encouraged to participate in IM Austria, IM Copenhagen, IM Brazil Florianopolis, and/or IM Barcelona in order to achieve a fast race time to qualify for the IM World Championship in IM Hawaii where the top race times were achieved. Most likely these races offer the best ambient temperatures for a fast race time.

Key factors influencing cycling performance and overall race time in the Ironman 70.3 for amateur athletes

Previous study has shown that cycling is the most predictive modality in the Ironman 70.3 triathlon distance. As a result, understanding the physiological and anthropometric variables that are mostly closely related to cycling performance can help coaches and athletes to direct their training programs. This study aimed to investigate the physiological, anthropometric, and general training characteristics influencing overall race time and cycling split time in Ironman 70.3. The present study also investigated the significance of body composition as a performance-related variable. A questionnaire was used to assess training characteristics in 12 athletes (six men and six women), body composition in dual X-ray absorptiometry, and physiological variables in an incremental cardiopulmonary test. Ironman 70.3 São Paulo–Brazil 2023 was completed by all participants. The relationship between performance and the variables measured were investigated, and a multiple regression model for cycling split time and overall race time was developed. Functional threshold power (FTP) can predict cycling split time in Ironman 70.3 (r2 = 0.638, p = 0.002). Maximal oxygen uptake (VO2max) (r2 = 0.667, p = 0.001) can predict overall race time. FTP and VO2max are also strongly related to lean mass and fat mass percentage. While FTP is the most important predictor of cycling split time, V˙O2max is the most important predictor of overall race time in an Ironman 70.3. Furthermore, because body composition (fat mass %) and muscle mass (kg) are variables strongly related to FTP and VO2max, we recommend that coaches and athletes consider to conduct a body composition assessment.