Training Characteristics of a World Championship 5000-m Finalist and Multiple Continental Record Holder Over the Year Leading to a World Championship Final

Purpose: Optimal training for endurance performance remains a debated topic. In this case study, the training of a world-class middle-/long-distance runner over a year's duration is presented. Methods: The training is analyzed via 2 methods to define training intensity distribution (TID) (1) by physiological zones and (2) by zones based on race pace. TID was analyzed over the full season, but also over the final 6, 12, and 26 weeks to allow for consideration of periodization/phases of season. The results of both methods are compared. Other training data measured include volume and number of sessions. Results: The average weekly volume for the athlete was 145.8 (24.8) km.wk(-1). TID by physiological analysis was polarized for the last 6 weeks of the season but was pyramidal when analyzed over the final 12, 26, and 52 weeks of the season. TID by race-pace analysis was pyramidal across all time points. The athlete finished 12th in the final of the World Championship 5000-m and made the semifinal of the 1500-m. He was ranked in the top 16 in the world for 1500, 5000, and 10,000 m. Conclusion: The results of this study demonstrate a potential flaw with recent work suggesting polarized training as the most effective means to improve endurance performance. Here, different analysis methods produced 2 different types of TID. A polarized distribution was only seen when analyzed by physiological approach, and only during the last 6 weeks of a 52-week season. Longer-term prospective studies relating performance and physiological changes are suggested.

Automated summary

This case study analyzed the training of a world-class middle-/long-distance runner over a year's duration using two methods. The study found potential flaws with the recent work suggesting polarized training as the most effective means to improve endurance performance, as different analysis methods produced different training intensity distributions.