The fourth edition of Cannero 6 ore took place on December 7. Beautifully situated on Lake Maggiore, the race consisted of laps of 2145 m in length and 10 m in elevation gain on granite slabs and cobblestones. For a €25 entry fee, participants enjoyed a perfectly organized competition, with the village priest sending the runners on their way, a large buffet, and, at the end, a large medal and a backpack full of natural products.
Read moreScholarGPS has updated Scholar Profiles and Rankings. Beat Knechtle is listed as a highly ranked scholar in the fields of anthropometry, life sciences, and physiology. His H-index is 60. He ranks first in half marathons and second in world championships and Ironman triathlons.
Read moreThe full evaluation can be found at Beat Knechtle | Scholar Profiles and Rankings | ScholarGPS
Individual factors related to performance in age group triathletes competing in different race distances have been explored in scientific literature. However, only a few studies have been conducted using machine learning (ML) predictive models to explore the importance of those individual factors. This study intended to build and analyze machine learning regression models that predict the performance of IRONMAN® 70.3 age group triathletes, considering sex, age, country of origin, and event location as predictive factors. A total of 823,464 finishers´ records (625,398 men and 198,066 women) of IRONMAN® 70.3 age group triathletes participating in 197 different events in 183 different locations between 2004 and 2020 were analyzed. The triathletes’ sex, age, country of origin, event location and year, and race finish times were thus obtained and considered for the study. Four different ML regression models were built to predict the triathletes’ race times from their age, sex, country of origin, and race location. The model with the best performance was then selected and further analyzed using model-agnostic interpretability tools to understand which factors would contribute most to the model predictions. The Random Forest Regressor model obtained the best predictive score. This model’s partial dependence plots indicated that men under 30 years, from Switzerland or Denmark, competing in IRONMAN®70.3 Austria/St. Polten, IRONMAN® 70.3 Switzerland, IRONMAN® 70.3 Sunshine Coast, and IRONMAN® 70.3 Busselton presented the best performance. Our results prove that ML models can be used to examine the complex, non-linear interactions between the factors that influence performance and gain insights that can help IRONMAN® 70.3 age group triathletes better plan their races.
Read moreThe Frauenfeld Marathon is a challenging 42.2-kilometer run with an elevation gain of 640 meters from Frauenfeld to Wil (SG) and back to Frauenfeld.
Read moreAlthough the run is very demanding, unfortunately there is only a very small medal.
On November 9, the Lake Maggiore Marathon took place once again in beautiful weather.
Read moreWith a pacemaker, I was about 10 minutes faster than last year. The 4-hour mark is within reach.
Anxiety is an important transdiagnostic factor for depression. Our purpose was to investigate the association between anxiety and depression symptoms among endurance runners. We used a cross-sectional, cross-country, web-based research design. A web survey was used to gather information about the runners’ general profile (age, sex, civil status, main sport, performance level, sports-specific characteristics, and training characteristics), anxiety symptoms (Generalized Anxiety Disorder-7), and depression symptoms (Patient Health Questionnaire-9). Network analysis was performed using the software JASP (Version 19). We sampled a total of 382 endurance men athletes, competing in ultramarathon (n = 226), marathon (n = 89), half-marathon (n = 55) or others. ANX_Q4 and DEPRESS_Q8 showed the highest strength and expected influence, indicating that are key bridge between anxiety and depressive symptoms and the rest of the network. Interventions that target relaxation capacity, body awareness, and recovery regulation could potentially attenuate the activation of both anxiety and depressive symptoms simultaneously.
Read moreOn November 2, 2025, the next Mostindien Marathon took place in the form of a double-decker event. Twenty-four hours after the 9th event, it was pouring rain.
Read moreAnd only one person started the marathon on both days.
The 9th Mostindien Marathon took place on November 1. The half marathon, marathon, and 50 km ultramarathon were run in mild autumn weather.
Read morePacing 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.
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