Hypoxia And Blood Flow Restriction Increases Peripheral Fatigue During Repeated Sprint Exercise
Bibliographic record
Abstract
PURPOSE: Peripheral muscle fatigue, acting directly and via its influence over central motor output, is a significant determinant of exercise performance. However, little is known about the influence of the muscle function in a hypoxic versus ischemic environment on the performance. This study examined the progression of peripheral fatigue during repeated sprint exercise (RSE) to exhaustion in environments of normoxia (N) and hypoxia (H) with the addition of occlusion via blood flow restriction (BFR). METHODS: 10 active individuals (age 27.7 ± 3.3 yrs; body mass 68.9 ± 11.8 kg; height 171.8 ± 4.3 cm) performed RSE (10-s maximal sprint followed by 20-s recovery) until exhaustion on a recumbent bike in 4 randomized conditions [normoxia, N; normoxia with 45% BFR (N-BFR); normobaric hypoxia simulating 3800 m (H); H with 45% BFR (H-BFR)]. During pre-RSE, after each 5 sprints (normalized, mid 20-40-60-80-100% of sprints), post-1 immediate, and post-2 3 min after RSE, pedals were blocked, and neuromuscular testing was performed. Maximum voluntary isometric contraction (MVIC) and evoked forces including high frequency doublet (Db100) and the ratio of electrical stimulation delivered at 10 Hz and 100 Hz (Db10:100) were measured. RESULTS: MVIC force reductions from pre- to post-RSE are seen in the figure. Condition main effects showed Db100 was greater in H-BFR than both N and N-BFR (128.8 ± 46.9, 114.4 ± 39.9, 114.6 ± 44.4 N, respectively, all p < 0.05). The Db10:100 ratio was greater in H-BFR than both N and N-BFR (78 ± 14, 74 ± 16, 74 ± 13%, respectively, all p < 0.05), possibly indicating low-frequency fatigue (LFF). CONCLUSIONS: Performing RSE to exhaustion demonstrated that, especially for BFR conditions, peripheral fatigue increases progressively and that the condition H-BFR elicits greatest levels of muscle fatigue. Regardless of normoxia or hypoxia, the addition of BFR increases total and peripheral fatigue, decreases force output, and increases the probability of LFF .
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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) | 0.001 | 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".