Identifying anxiety and sleep problems, associated factors and sex differences in endurance and ultra-endurance runners

Anxiety and sleep problems may negatively impact health and athletic performance. We conducted a cross-sectional survey study in endurance (≥21.1-42.2 km) and ultra-endurance runners (≥42.2 km), screening for anxiety and sleep problems, assessing potential associated factors and sex differences. Statistical methods included descriptive statistics, testing of group differences with the Kruskal-Wallis H-test, and Dunn’s post-hoc tests, allowing for Bonferroni correction for multiple comparisons, predictive techniques, and regression analysis. A total of 601 runners participated (female n = 222; male n = 379; mean age 42.8 ± 10.1 years). Overall, 13.5% screened positive for anxiety (female 16.2% compared to men 11.9%; n.s.) and 28.8% for sleep problems (female 32.9% compared to men 26.4%; n.s.). Anxiety and sleep problems were observed significantly more often in half marathon runners (25.2%; (p < 0.001) and 38.3%, (p = 0.02), respectively) compared to marathon (9.8 and 28.4%) and ultramarathon distance runners (11.1 and 28.2%). No statistical differences were found between sexes and performance levels (elite versus non-elite). Associated factors for anxiety included sleep problems (p < 0.001), younger age (<29 years; p < 0.001), years practicing the sport (>10 years; p = 0.006), and distance category (p = 0.03). Associated factors for sleep problems included anxiety (p < 0.001), competition frequency (>4 per year; p = 0.006), and injury-related absences (p = 0.001). Mental health issues, such as anxiety and sleep problems are common in endurance and ultra-endurance runners and positive screening for anxiety co-existed and was associated with positive screening for sleep problems. This study demonstrates that identifying and screening for anxiety and sleep problems is important, as well as the need for creating awareness, education, preventative strategies, and support services.

Change in elevation predicts 100 km ultra marathon performance

The 100-km ultra-marathon is one of the most popular ultra-marathon distances. While we have a lot of scientific knowledge, no data exist about the influence of race course characteristics and other geographical aspects, on race performance. Therefore, the aims of this study were (i) to investigate where the fastest 100-km races are held and where the fastest runners originate from, (ii) to evaluate a potential influence of specific race characteristics (i.e., influence of elevation and race course characteristics) on performance, and (iii) to assess the influence of individual athlete performance against the other investigated factors. A total of 858,544 race records (732,748 from men and 125,796 from women) from 317,312 unique runners originating from 103 different countries and participating in 2,648 100-km races held in 80 different countries worldwide between 1892 and 2022 were analyzed using several descriptive, inferential and predictive methods, including a machine learning XG Boost Regression model. We evaluated the influence on the average running speed (in km/h) of factors such as gender of the athlete, age group, country of origin of the athlete, country where the race was held, course characteristics (i.e. mountain, trail, road, or track race) and elevation (i.e. flat or hilly course). The relative effect of the individual athlete performance was also investigated through a Mixed Effects Linear model. Discounting the fact that individual athlete performance is between 3 and 4 times ahead in race speed influence compared to the other factors, the model rated elevation (0.85) as the most important variable ahead of the country where the race was held (0.07), gender (0.02), age group (0.02), the country of origin of the runner (0.02) and the course characteristics (0.02). Running on a track (9.32 km/h) was the fastest ahead of road running (8.11 km/h), trail running (6.21 km/h) and mountain running (5.74 km/h). Flat running (8.85 km/h) was faster than running on a hilly course (6.57 km/h). The fastest athletes originated from African and Eastern European countries, with Swaziland (13.15 ± 0.88 km/h), Botswana (11.61 ± 2.22 km/h), Belarus (11.10 ± 2.29 km/h), Kazakhstan (10.74 ± 3.78 km/h), and Cape Verde (10.49 ± 2.26 km/h) in the top five. Africa, the Middle East, and Europe hold the fastest 100 km races, with Botswana (12.23 ± 1.35 km/h), Qatar (12.10 ± 1.63 km/h), Belarus (11.24 ± 1.27 km/h), Jordania (11.05 ± 1.58 km/h), and Montenegro (10.63 ± 1.90 km/h) in the top five. In summary, elevation was the most important variable in 100-km ultra-marathon running ahead of the country where the race was held, gender, age group, country of origin of the runner and course characteristics. Running on a track was the fastest ahead of road, trail and mountain running. Flat running was faster than running on a hilly course. Africa, the Middle East, and Europe hold the fastest 100 km races. Common for the fastest 100-km race courses was the fact that they were mainly indoor races and/or Championships. The fastest runners originated mainly from former republics of the dissolved Soviet Union. Future studies might select the fastest 100-km race courses.

Finding the fastest race locations for non-elite IRONMAN® age group triathletes

The IRONMAN® triathlon is particularly interesting for recreational (age group) triathletes, where tens of thousands compete annually to qualify for the IRONMAN® World Championship in Hawaii. The purpose of the present study was to identify the fastest event location for age group triathletes. A total of 687,662 finisher records of IRONMAN® age group triathletes from 446 events at 65 different locations between 2002 and 2022 were analyzed, aggregating records by location and calculating and displaying descriptive statistics. The statistical significance of the differences observed was tested using a twoway ANOVA (sex and event location as independent variables, overall race times or split times as dependent variables) and post-hoc Tukey’s HSD tests. The fastest swim times were achieved in IRONMAN® New York, ahead of IRONMAN® Switzerland Thun and IRONMAN® Chattanooga for both men and women. There were differences between women and men regarding the fastest cycling and running courses. The fastest cycling splits were in IRONMAN® Barcelona, followed by IRONMAN® Copenhagen and IRONMAN® Tallinn for men and IRONMAN® Barcelona, IRONMAN® Copenhagen and IRONMAN® Vitoria-Gasteiz for women. For the marathon, men achieved the fastest running split in IRONMAN® Hawaii, ahead of IRONMAN® Vitoria-Gasteiz and IRONMAN® Tallinn, whereas women were the fastest in running in IRONMAN® Gdynia, IRONMAN® Haugesund Norway and IRONMAN® Hawaii. For overall race times, men achieved their times in IRONMAN® Hawaii, followed by IRONMAN® Vitoria-Gasteiz and IRONMAN® Copenhagen. For women, the fastest overall race times were achieved in IRONMAN® Vitoria-Gasteiz ahead of IRONMAN® Hawaii and IRONMAN® Copenhagen. For overall race times, average water temperatures were at 20.7 ± 2.8 °C and average air temperatures at 23.0 ± 3.0 °C. Most swimming courses were in a lake (7/10), most cycling courses were rolling (6/10) and most running courses were flat (7/10). Despite differences regarding the event locations, the fastest race courses were identified in the USA and Spain.

An analysis of 12-hour ultramarathon performance

Ultra-marathon running is highly popular, with races including distance-limited, time-limited, and multistage events. The 12-hour run is the second shortest time-limited ultra-marathon, though little is known about the origin of athletes or where these races are preferably held. Therefore, the present study investigated where the fastest 12-hour runners originate from and where the fastest 12-hour race courses are located. A machine learning model based on the XG Boost algorithm was developed to predict running speed based on athlete age, sex, country of origin, and the country where the races were held. After the model was built and trained, explainability tools were used to investigate how each independent variable influenced the predicted running speed. A total of 103,334 race records of 53,700 unique runners from 69 countries participating in races held in 55 countries were analysed. The United States of America (USA) accounted for about one-third of the 12-hour race records for country of origin and country of the event, followed by Taiwan, several European countries (e.g., Germany, France, Italy, and Norway), and others from the Anglosphere (e.g., Australia, United Kingdom, and South Africa). Athletes from Lithuania, Israel, Russia, Hungary, Croatia, and Namibia achieved the fastest average running speeds. The fastest running speeds were achieved in races held in Russia, the Netherlands, Israel, Slovakia, the Czech Republic, Croatia, and Hungary. There was a positive correlation between country of origin and country of event, indicating that athletes competed mainly in their home country. Men were about 0.5 km/h faster than women on average. Most athletes were in the 45–49 age group, while the fastest runners were in the 40–44 and 45–49 age groups. Most 12-hour ultra-marathon athletes originated from the USA and competed in the USA. However, athletes from Lithuania, Israel, Russia, Hungary, Croatia, and Namibia achieved the fastest running speeds.

Case study of 100 consecutive IRONMAN®-distance triathlons—impact of race splits and sleep on the performance of an elite athlete

Long-distance triathletes such as IRONMAN® and ultra-triathletes competing in longer race distances continue to extend ultra-endurance limits. While the performance of 60 IRONMAN®-distance triathlons in 60 days was the longest described to date, we analysed in the present case study the impact of split disciplines and recovery in one athlete completing 100 IRONMAN®-distance triathlons in 100 days. To date, this is the longest self-paced world record attempt for most daily IRONMAN®-distance triathlons. To assess the influence of each activity’s duration on the total time, the cross-correlation function was calculated for swimming, cycling, running, and sleeping times. The autocorrelation function, which measures the correlation of a time series with itself at different lags, was also employed using NumPy. The moving average for swimming slightly increased in the middle of the period, stabilizing at ∼1.43 h. Cycling displayed notable fluctuations between ∼5.5 and 7h, with a downward trend toward the end. The moving average for running remains high, between 5.8 and 7.2 h, showing consistency over the 100 days. The moving average for total time hovered at ∼15 h, with peaks at the beginning, and slightly declined in the final days. The cross-correlation between swimming time and total time showed relatively low values. Cycling demonstrated a stronger correlation with total time. Running also exhibited a high correlation with total time. The cross-correlation between sleep time and swimming time presented low values. In cycling, the correlation was stronger. For running, a moderate correlation was observed. The correlation with total time was also high. The autocorrelation for swimming showed high values at short lags with a gradual decrease over time. For cycling, the autocorrelation also began strong, decreasing moderately as lags increased. Running displayed high autocorrelation at short lags, indicating a daily dependency in performance, with a gradual decay over time. The total time autocorrelation was high and remained relatively elevated with increasing lags, showing consistent dependency on cumulative efforts across all activities. In a triathlete completing 100 IRONMAN®-distance triathlons in 100 days, cycling and running split times have a higher influence on overall times than swimming. Swimming performance is not influenced by sleep quality, whereas cycling performance is. Swimming times slowed faster over days than cycling and running times. Any athlete intending to break this record should focus on cycling and running training in the pre-event preparation.

Deca Marathon Colmar

Vom 26. Juni bis 5. Juli 2025 fand in Colmar der Deca Marathon statt. Es galt 10 Marathons in 10 Tagen zu laufen.

Mit dem 4. Gesamtrang resultierte die bis anhin beste Rangierung in einem 10 in10.

Auf das Podest fehlten nur rund 40 Minuten.

Sex-based performance analysis in Olympic triathlon: swimming, cycling, and running at Paris 2024

This study aimed to analyze sex differences in performance across the disciplines of the Olympic triathlon at the 2024 Paris Olympic Games. Performance times in swimming, cycling, running and transitions (T1 and T2) were compared between male (n = 50) and female (n = 51) athletes. Data were extracted from the official Olympic website and analyzed using the Mann–Whitney U test with effect size (Cohen’s d). Quantile regression was applied to examine the relationship between total race time and performance in each discipline of the Olympic triathlon. Male athletes outperformed females across all segments, including swimming, cycling, running, and transitions (p < 0.001). Cycling accounted for the largest proportion of total race time in both sexes (49.4% for females, 48.2% for males), while the contribution of running was slightly higher in males (29.8%) than in females (29.5%). Quantile regression revealed that cycling was the most influential predictor of total time among males, whereas running had greater impact among females, particularly in slower athletes (q = 0.75). Swimming was a consistent but less prominent predictor in both sexes, especially among faster athletes (q = 0.25). Transitions had limited influence in males but showed significant associations with performance among females at specific quantiles, notably in T2. These findings underscore the need for sex-specific training strategies, emphasizing running development in female triathletes and cycling optimization in males, while also considering the role of transitions, especially in draft-legal events.

Appenzeller Bärli Biber

Während andere für ihre Wettkämpfe spezielle Pülverchen und ähnliches verwenden setzt der Appenzeller auf Appenzeller Bärli Biber für die ganz harten Wettkämpfe

Bärli Biber Shop

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.