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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.