Training load and intensity distribution for sprinting among world-class track cyclists
Bibliographic record
Abstract
BACKGROUND: Only few studies analyzed real training programs of sprinters while that should be a valuable step in the understanding of sprint training. The present study aimed at characterizing track cycling sprinter training by training load and intensity distribution. METHODS: Twenty-nine weeks of prechampionship training data were retrospectively analyzed for 6 world-class athletes. Training load was measured by the ratio of volume completed to maximal volume and categorized by five intensity zones (endurance: zones1-2; sprinting: zones3-5) and exercise type (on-bike or resistance). Intra-week (training monotony) and inter-week (acute-chronic workload ratio) variation was also studied. RESULTS: On-bike training represented 77.4±15.3% of total training load; resistance training, 22.6±15.2% (note high standard deviation). Total weekly training load significantly varied (P=0.0002) with high acute-chronic workload ratio (12.0±3.2 weeks >1.5 or <0.8), but low intra-week variations (training monotony, 1.81±0.20). Zone4 and zone5 made up 74.4±16.9% of total training load; zone1, 15.8±11%. Training load was seldom in zone2 (6.4±5.3%) or zone3 (3.3±4.2%). From the first to the second half of the period, zone3-4 training load decreased (39.3±3.3 to 27.4±1.7%; P=0.01), while zone5 increased (34.9±2.4 to 50±3.7%; P=0.002). CONCLUSIONS: In this reduced group of elite athletes, training appeared to mainly consist of on-bike exercises within the highest intensity zones. As demonstrated by monotony and acute-chronic workload ratio overloading and unloading are based on high variations over weeks, not days. Essentially, this study describes a polarized intensity distribution on the highest intensities which increased with world championships approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".