Low-load resistance training to task failure with and without blood flow restriction: muscular functional and structural adaptations
Bibliographic record
Abstract
The application of blood flow restriction (BFR) during resistance exercise is increasingly recognized for its ability to improve rehabilitation and for its effectiveness in increasing muscle hypertrophy and strength among healthy populations. However, direct comparison of the skeletal muscle adaptations to low-load resistance exercise (LL-RE) and low-load BFR resistance exercise (LL-BFR) performed to task failure is lacking. Using a within-subject design, we examined whole muscle group and skeletal muscle adaptations to 6 wk of LL-RE and LL-BFR training to repetition failure. Muscle strength and size outcomes were similar for both types of training, despite ~33% lower total exercise volume (load × repetition) with LL-BFR than LL-RE (28,544 ± 1,771 vs. 18,949 ± 1,541 kg, P = 0.004). After training, only LL-BFR improved the average power output throughout the midportion of a voluntary muscle endurance task. Specifically, LL-BFR training sustained an 18% greater power output from baseline and resulted in a greater change from baseline than LL-RE (19 ± 3 vs. 3 ± 4 W, P = 0.008). This improvement occurred despite histological analysis revealing similar increases in capillary content of type I muscle fibers following LL-RE and LL-BFR training, which was primarily driven by increased capillary contacts (4.53 ± 0.23 before training vs. 5.33 ± 0.27 and 5.17 ± 0.25 after LL-RE and LL-BFR, respectively, both P < 0.05). Moreover, maximally supported mitochondrial respiratory capacity increased only in the LL-RE leg by 30% from baseline ( P = 0.006). Overall, low-load resistance training increased indexes of muscle oxidative capacity and strength, which were not further augmented with the application of BFR. However, performance on a muscle endurance test was improved following BFR training.
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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) | 0.001 | 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) | 0.002 | 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".