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.
Mark Kenneally, Arturo Casado, Josu Gomez-Ezeiza, and Jordan Santos-Concejero
Josu Gomez-Ezeiza, Jordan Santos-Concejero, Jon Torres-Unda, Brian Hanley, and Nicholas Tam
Purpose: To analyze the association between muscle activation patterns on oxygen cost of transport in elite race walkers over the entire gait waveform. Methods: A total of 21 Olympic race walkers performed overground walking trials at 14 km·h−1 where muscle activity of the gluteus maximus, adductor magnus, rectus femoris, biceps femoris, medial gastrocnemius, and tibialis anterior were recorded. Race walking economy was determined by performing an incremental treadmill test ending at 14 km·h−1. Results: This study found that more-economical race walkers exhibit greater gluteus maximus (P = .022, r = .716), biceps femoris (P = .011, r = .801), and medial gastrocnemius (P = .041, r = .662) activation prior to initial contact and weight acceptance. In addition, during the propulsive and the early swing phase, race walkers with higher activation of the rectus femoris (P = .021, r = .798) exhibited better race walking economy. Conclusions: This study suggests that the neuromuscular system is optimally coordinated through varying muscle activation to reduce the metabolic demand of race walking. These findings highlight the importance of proximal posterior muscle activation during initial contact and hip-flexor activation during early swing phase, which are associated with efficient energy transfer. Practically, race walking coaches may find this information useful in the development of specific training strategies on technique.
Simon J. de Waal, Josu Gomez-Ezeiza, Rachel E. Venter, and Robert P. Lamberts
Purpose: To provide a systematic overview of physiological parameters used to determine the training status of a trail runner and how well these parameters correlate with real-world trail running performance. Method: An electronic literature search of the PubMed and Scopus digital databases was performed. Combinations of the terms “trail run” or “trail runner” or “trail running” and “performance” were used as search terms. Seven studies met the inclusion criteria. Results: Trail running performance most commonly correlated (mean [SD]) with maximal aerobic capacity (71%; r = −.50 [.32]), lactate threshold (57%; r = −.48 [.28]), velocity at maximal aerobic capacity (43%; r = −.68 [.08]), running economy (43%; r = −.31 [.22]), body fat percentage (43%; r = .55 [.21]), and age (43%; r = .52 [.14]). Regression analyses in 2 studies were based on a single variable predicting 48% to 60% of performance variation, whereas 5 studies included multiple variable regression analyses predicting 48% to 99% of performance variation. Conclusions: Trail running performance is multifaceted. The classic endurance model shows a weaker association with performance in trail running than in road running. Certain variables associated with trail running research (such as testing procedures, race profiles, and study participants) hinder the execution of comparative studies. Future research should employ trail-specific testing protocols and clear, objective descriptions of both the race profile and participants’ training status.