Resistance Training with and Without Blood Flow Restriction to Repetition Failure: More Pain, Same Gain
Bibliographic record
Abstract
Evidence suggests blood flow restricted (BFR) resistance training performed with low-loads (20-40% 1-repetition maximum; 1-RM) is superior to low-load training when volume (load x repetitions) is matched. Since it has been shown using traditional resistance training that similar gains in muscle strength and hypertrophy occur between high- and low-load training when performed to repetition failure, it is of interest if this also occurs with low-load training with/without BFR. Moreover, the perception of pain at repetition failure between protocols and over time has not been examined in a training setting. PURPOSE: To determine if low-load resistance training to repetition failure with/without BFR elicits similar muscular strength, hypertrophy and perceived pain. METHODS: Seven young (25±1 yr) males were recruited to perform single-leg Smith-machine squats 3 d/wk for 6 wk. Each leg was randomly assigned to perform 30% 1-RM with (BFR) or without BFR (RT) for 3 sets to repetition failure with 100s of rest after each set. Tourniquet pressure was set at 60-70% of the lowest occlusive pressure and remained inflated throughout the 3 sets. Leg strength (1-RM), muscle hypertrophy (leg lean mass; LLM) by dual-energy X-ray absorptiometry, and ultrasound derived vastus lateralis (VL) muscle thickness (MT), were measured before and after the 6-weeks. A visual analog scale (1000 point) was used to assess pain after each set and rest period for the 1st, 4th, 8th, 11th and 15th training session. RESULTS: 1-RM increased similarly in both groups after training (BFR 79±13 to 95±13 kg vs. RT 82±13 to 100±13 kg, p<0.002) and VL MT (BFR: 2.69±0.08 to 2.98±0.1 vs. RT: 2.75±0.16 to 2.96±0.1 cm, p<0.016) with non-significant changes in LLM (BFR 7.29±0.38 to 7.40±0.39 vs. RT 7.28±0.37 to 7.34±0.36 kg, p<0.243). There was an increase in perceived pain with BFR training compared to the RT group across all sessions following the first rest period (BFR: 288±25 vs. RT: 155±9 a.u., p<0.05) and second rest period (BFR: 433±31 vs. RT:160±9 a.u., p<0.05). While there was a trend for a decrease in pain over time with repeated training, this effect was non-significant. CONCLUSIONS: When performed to failure, low-load training with and without BFR have similar muscle strength and hypertrophy despite differences in perceived pain. Supported by NSERC, CFI and ERA
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".