Whole-body active warm-up and inspiratory muscle warm-up do not improve running performance when carrying thoracic loads
Bibliographic record
Abstract
Whole-body active warm-ups (AWU) and inspiratory muscle warm-up (IMW) prior to exercise improves performance on some endurance exercise tasks. This study investigated the effects of AWU with and without IMW upon 2.4-km running time-trial performance while carrying a 25-kg backpack, a common task and backpack load in physically demanding occupations. Participants (n = 9) performed five 2.4-km running time-trials with a 25-kg thoracic load preceded in random order by (i) IMW comprising 2 × 30 inspiratory efforts against a pressure-threshold load of 40% maximal inspiratory pressure (PImax), (ii) 10-min unloaded running (AWU) at lactate turnpoint (10.33 ± 1.58 km·h−1), (iii) placebo IMW (PLA) comprising 5-min breathing using a sham device, (iv) AWU+IMW, and (v) AWU+PLA. Pooled baseline PImax was similar between trials and increased by 7% and 6% following IMW and AWU+IMW (P < 0.05). Relative to baseline, pooled PImax was reduced by 9% after the time-trial, which was not different between trials (P > 0.05). Time-trial performance was not different between any trials. Whole-body AWU and IMW performed alone or combination have no ergogenic effect upon high-intensity, short-duration performance when carrying a 25-kg load in a backpack.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".