Combined effects of weight reduction and hypoxia on physiological and perceptual responses to high-intensity exercise in endurance athletes
Bibliographic record
Abstract
Introduction This study investigated the effects of body weight (BW) reduction and hypoxia on physiological and perceptual responses during high-intensity interval exercise (HIIE) on the anti-gravity AlterG® treadmill.Material & methods Twenty-six participants (12 women, age: 26.2 years, height: 170.4 cm, weight: 67.8 kg, VO2max: 61.1 mL/min/kg) completed a HIIE in 5 randomized conditions: normoxia at 100%BW; normoxia at 80%BW; normoxia at 60%BW; hypoxia (FIO2 = 0.14) at 80%BW; and hypoxia at 60%BW. The HIIE included 3 sets of 8 × 30-s efforts interspersed with 30-s rest at 110% peak treadmill speed. Heart rate (HR), pulse arterial O2 saturation (SpO2), muscle deoxyhemoglobin concentration ([HHb]), and rate of perceived exertion (RPE) were continuously recorded. Blood lactate concentration ([Lac−]) was measured post-session.Results BW reduction decreased HR, [Lac−] and RPE compared to control (p < 0.05) and [HHb] in men at the lowest %BW. When hypoxia was added, SpO2 was reduced from 98% to 85%. HR remained lower in all conditions compared to control (p < 0.05). RPE and [HHb] were higher in hypoxia than normoxic equivalent conditions (p < 0.05). [Lac−] was higher in Hyp80% compared to other conditions for men (p < 0.05). Despite subtle differences, men and women responded similarly to this exercise–environment combination.Conclusion Hypoxia effectively restored physiological stress during HIIE despite BW reduction, primarily impacting systemic rather than local muscular physiological parameters. This combination of methods may be beneficial in the rehabilitation and performance context.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".