Women in the triathlon—the differences between female and male triathletes: a narrative review

Triathlon events have gained popularity in recent years. With the increasing participation of women, aspects that influence performance and physiology, as well as differences between women and men, are of interest to athletes and coaches. A review of the existing literature concerning differences between women and men in triathlon is lacking. Therefore, this narrative review aimed to compare female and male triathletes in terms of participation, performance, and the different influences on performance (e.g., physiology, age, pacing, motivation). A literature search was conducted in PubMed and Scopus using the search terms “female triathletes”, “women in triathlon”, “triathlon AND gender difference”, and “triathlon AND sex difference”. 662 articles were found using this search strategy, of which 147 were relevant for this review. All distances from sprint to ultra-triathlon (e.g., x-times IRONMAN® distance) were analyzed. The results showed that the participation of female triathletes, especially female master triathletes increased over time. An improvement in the performance of female and older triathletes was observed at the different distances in the last decades. Sex differences in performance varied across distances and in the three disciplines. Female triathletes showed a significantly lower VO2max and higher lactate thresholds compared to men. They also had a higher body fat percentage and lower body mass. The age for peak performance in the IRONMAN® triathlons is achieved between 25 and 39 years for both women and men. Strong predictors of IRONMAN® race performance in both female and male triathletes include achieving a personal best time in a marathon and a previous best time in triathlon races. Further studies need to balance the representation of female and male athletes in study cohorts to ensure that findings are relevant to both sexes. Another research gap that should be addressed by future studies is the effect of menstruation and female hormones, the presence of premenstrual syndrome, and the impact of pregnancy and childbirth on the triathlon performance to better understand the differences with men and to account for hormonal fluctuations in training.

Marathons

After Beat Knechtle broke Frenchman Guy Rossi’s record for the most long-distance triathlons worldwide, the next record chase awaits. Now it’s all about the most marathons ever run by a Swiss.

Christian Marti’s record of 600 marathons is the next target.

2nd Eastern Switzerland Marathon Days

The 2nd Eastern Switzerland Marathon Days took place from May 1 to 11, 2025. Beat Knechtle was once again the only runner to complete all 11 marathons, as he did in 2024 at the 1st Eastern Switzerland Marathon Days.

2nd Eastern Switzerland Marathon Days Gallery

11 marathons in a row: Appenzell extreme athletes at the Eastern Switzerland Marathon Days

Eleven days in a row, 42 kilometers every day: Appenzell doctor Beat Knechtle is running eleven marathons in a row at the Eastern Switzerland Marathon Days. While many people reach their limits in just one, for the doctor from Eastern Switzerland it’s just an easy run-in.

11 Marathons am Stück: Appenzeller Extremsportler bei den Ostschweizer Marathontagen

Congratulations on being named a 2024 Highly Ranked Scholar by ScholarGPS

ScholarGPS® is the world’s most comprehensive scholarly analytics platform, built by scholars but accessible to all. ScholarGPS® is comprised of powerful computational systems and processes such as novel data mining, artificial intelligence, machine learning, statistical analysis, as well as data distillation, interpretation, and presentation. ScholarGPS provides detailed profiles for each of over 30 million scholars and 120,000 research institutions, including over 24,000 academic institutions in more than 200 countries.

Beat Knechtle is among the highly ranked scholars with position 384 for Life Sciences, position 10 for Physiology and position 8 for Running Beat Knechtle | Scholar Profiles and Rankings | ScholarGPS

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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.