Live-high train-low improves repeated time-trial and Yo-Yo IR2 performance in sub-elite team-sport athletes
Bibliographic record
Abstract
ObjectivesTo determine the efficacy of live-high train-low on team-sport athlete physical capacity and the time-course for adaptation.DesignPre-post parallel-groups.MethodsFifteen 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 (FIO2: 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 (Hbmass) 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.ResultsCompared to pre, Hbmass 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).ConclusionsFifteen nights of LHTL was possibly effective, while 19 nights was effective at increasing Hbmass, Yo-Yo IR2 and repeated TT performance more than sea-level training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".