Knechtle Beat, Leite Luciano Bernardes, Forte Pedro, Andrade Marilia Santos, Cuk Ivan, Nikolaidis Pantelis T, Scheer Volker, Weiss Katja, Rosemann Thomas
Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland.
Institute of Primary Care, University Hospital Zurich, Zurich, Switzerland.
Front Sports Act Living. 2025 Jun 26;7:1554342. doi: 10.3389/fspor.2025.1554342. eCollection 2025.
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.
像IRONMAN®赛事的长距离铁人三项运动员以及参加更长赛程的超级铁人三项运动员不断挑战极限耐力。虽然在60天内完成60场IRONMAN®距离的铁人三项赛是迄今为止报道的最长赛程，但在本案例研究中，我们分析了一名运动员在100天内完成100场IRONMAN®距离的铁人三项赛时，分项赛事和恢复情况的影响。迄今为止，这是在一天内完成最多场IRONMAN®距离铁人三项赛的最长自主 paced世界纪录尝试。
为评估每项活动时长对总时长的影响，计算了游泳、骑行、跑步和睡眠时间的互相关函数。还使用NumPy计算了自相关函数，该函数用于测量时间序列在不同滞后时与其自身的相关性。
游泳的移动平均值在赛程中期略有增加，稳定在约1.43小时。骑行在约5.5至7小时之间波动明显，接近赛程结束时呈下降趋势。跑步的移动平均值保持在较高水平，在5.8至7.2小时之间，在100天内保持稳定。总时长的移动平均值徘徊在约15小时，在开始时出现峰值，在最后几天略有下降。游泳时间与总时长之间的互相关值相对较低。骑行与总时长的相关性更强。跑步与总时长也呈现出高度相关性。睡眠时间与游泳时间之间的互相关值较低。在骑行方面，相关性更强。对于跑步，观察到中度相关性。与总时长的相关性也很高。游泳的自相关在短滞后时显示出高值，随着时间逐渐下降。对于骑行，自相关也开始时很强，随着滞后增加适度下降。跑步在短滞后时显示出高自相关性，表明每日表现存在依赖性，随着时间逐渐衰减。总时长的自相关性很高，随着滞后增加保持相对较高水平，表明对所有活动的累积努力存在一致的依赖性。
在一名100天内完成100场IRONMAN®距离铁人三项赛的铁人三项运动员中，骑行和跑步的分项时间对总时间的影响大于游泳。游泳表现不受睡眠质量影响，而骑行表现受其影响。游泳时间在数天内比骑行和跑步时间减慢得更快。任何打算打破这一纪录的运动员在赛前准备中都应专注于骑行和跑步训练。
