Live-high train-low improves repeated time-trial and Yo-Yo IR2 performance in sub-elite team-sport athletes
Bibliographic record
Abstract
Objectives To determine the efficacy of live-high train-low on team-sport athlete physical capacity and the time-course for adaptation. Design Pre-post parallel-groups. Methods Fifteen Australian footballers were matched for Yo-Yo Intermittent recovery test level 2 (Yo-YoIR2) performance and assigned to LHTL ( n =7) or control (Con; n =8). LHTL spent 19 nights (3×5 nights, 1×4 nights, each block separated by 2 nights at sea level) at 3000-m simulated altitude (F I O 2 : 0.142). Yo-Yo IR2 was performed pre and post 5, 15, and 19 nights. A 2- and 1-km time-trial (TT) was performed pre and post intervention. Haemoglobin mass (Hb mass ) was measured in LHTL after 5, 10, 15, and 19 nights. A contemporary statistical approach using effect size, confidence limits, and magnitude-based inferences was used to measure changes between groups. Results Compared to pre, Hb mass was possibly higher after 15 (3.8%, effect size (ES) 0.19, 90% confidence limits 0.05–0.33) and very likely higher after 19 nights (6.7%, 0.35, 0.10; 0.52). For Yo-Yo IR2, LHTL group change was not meaningfully different to Con after 5 nights, possibly greater after 15 (10.2%, 0.37, −0.29; 1.04), and likely greater after 19 nights (13.5%, 0.49, −0.16; 1.14). Both groups improved 2-km TT, with LHTL improvement possibly higher than CON (1.9%, 0.22, −0.18; 0.62). Only LHTL improved 1-km TT, with LHTL improvement likely greater than CON (4.6%, 0.56, −0.08; 1.04). Conclusions Fifteen nights of LHTL was possibly effective, while 19 nights was effective at increasing Hb mass , Yo-Yo IR2 and repeated TT performance more than sea-level training.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.004 | 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".