Perspectives of World-Class Endurance Coaches on the Evolution of Athlete Training and Performance
Bibliographic record
Abstract
PURPOSE: To provide insights into the key practices driving the evolution of endurance training and performance. METHODS: A total of 78 world-class coaches (73 men, 5 women), representing 14 endurance sports, and 18 nations, participated in a digital survey comprising open-ended questions about recent trends and projected future developments. RESULTS: Qualitative thematic analysis revealed 8 key drivers of change: (1) individualized and sport-specific training strategies, (2) precision in training execution, (3) load-management procedures, (4) strategic use of environmental stressors, (5) optimized nutrition, (6) holistic recovery practices, (7) health and injury prevention, and (8) equipment and technology-driven innovation. To provide a clearer conceptual framework, these themes were grouped into 3 overarching categories: training methodologies (themes 1-4), recovery and health management (themes 5-7), and technological innovation (theme 8). CONCLUSIONS: World-class endurance coaches describe a continuing shift toward more individualized training strategies, characterized by detailed sport-specific considerations, training plans aligned with physiological profiles, greater precision in training execution, refined load management, and strategic use of environmental stressors. In this context, advanced monitoring technologies are viewed as essential for optimizing training adaptations while minimizing the risk of maladaptation and injury. Coaches also emphasized the importance of enhanced health and recovery strategies to support training adaptations, including sleep quality, stress management, life balance, and targeted nutritional interventions, particularly carbohydrate availability. Finally, the rapid development of new equipment and technologies is transforming training and coaching practices, thereby contributing to improved endurance performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".