Effects of high-intensity running training on soccer-specific fitness in professional male players
Bibliographic record
Abstract
The purpose of this study was to investigate whether or not physiological and performance gains could be achieved with the addition of high-intensity running to an existing training programme in a group of well trained professional male soccer players. Sixteen professional male players (21.3 ± 2.1 years, stature 177.4 ± 4.2 cm, body mass 73.1 ± 8.1 kg) were randomised in training (TRA, n = 8) and control (CON, n = 8) groups. All players performed physiological assessments before and after a 6-week intervention. Outcome measures were: (i) V̇O2peak, (ii) V̇O2 kinetics during very heavy-intensity exercise, (iii) a maximal anaerobic running test, and (iv) Yo-Yo Intermittent Recovery Test level 2 (YIRT2). The only aerobic parameter to change after the intervention was the phase III time constant at exercise onset for CON, which lengthened (p = 0.012) to a value similar to that of the TRA group. However, TRA showed gains in anaerobic performance (p = 0.021), time to exhaustion (p = 0.019), and maximal running speed (p = 0.023). In the YIRT2, distance run increased for TRA over time (p = 0.015), and the TRA group were also capable of running further in the YIRT2 after the intervention compared with CON (p = 0.011). This study shows it is possible to improve the soccer-specific high-intensity running capacity of professional players when high-intensity intermittent training is added to the normal training load and that this effect is only detectable in anaerobic capabilities. The observed effects are meaningful to the training practices of elite athletes seeking a competitive edge in team sports when otherwise well matched.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".