Low‐ and high‐volume blood‐flow restriction treadmill walking both improve maximal aerobic capacity independently of blood volume
Bibliographic record
Abstract
Abstract Aim Assess the effect of low‐ and high‐volume blood flow restriction training (BFR) on maximal aerobic capacity (VO2max) and determine if alteration in VO2max is mediated through changes in hemoglobin mass (Hbmass) and blood volume. Methods Participants' Hbmass (CO‐rebreathe), single, and double‐leg VO2max and blood volume regulating hormonal responses (renin and copeptin) were measured before and after BFR training. Training consisted of treadmill walking either (1) twice‐daily for 4week (CON and BFRHV) or (2) twice‐weekly for 6week (BFRLV). Each session consisted of five intervals (3 min, 5% incline, 5 km/h, 100% of lowest occlusion pressure), with 1 min of standing rest between sets. Results VO2max increased using both training exposures, in as quickly as 2‐weeks (BFRLV baseline to 4week: +315 ± 241 mL (8.7%), p = 0.02; BFRHV baseline to 2week: +360 ± 261 mL (7.9%), p < 0.01), for the BFRLV and BFRHV groups, with no change in CON. Single‐ and double‐leg VO2max improved proportionately (single/double‐leg VO2max ratio: BFRLV 78 ± 4.9–78 ± 5.8%, BFRHV 79 ± 6.5–77 ± 6.5%), suggesting that the mechanism for increased VO2max is not solely limited to central or peripheral adaptations. Hbmass remained unchanged across groups (CON: +10.2 ± 34 g, BFRLV: +6.6 ± 42 g, BFRHV: +3.2 ± 44 g; p = 0.9), despite a significant release of blood volume regulating hormones after initial BFR exposure (renin +20.8 ± 21.9 ng/L, p < 0.01; copeptin +22.0 ± 23.8 pmol/L, p < 0.01), which was blunted following BFRHV training (renin: +13.4 ± 12.4 ng/L, p = 0.09; copeptin: +1.9 ± 1.7 pmol/L, p = 0.98). Conclusion BFR treadmill walking increases VO2max irrespective of changes in Hbmass or blood volume despite a large release of blood volume regulating hormones in response to BFR treadmill walking.
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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".