Myoelectric Activity and Fatigue in Low-Load Resistance Exercise With Different Pressure of Blood Flow Restriction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Low-load resistance exercise (LL-RE) with blood flow restriction (BFR) promotes increased metabolic response and fatigue, as well as more pronounced myoelectric activity than traditional LL-RE. Some studies have shown that the relative pressure applied during exercise may have an effect on these variables, but existing evidence is contradictory. Purpose: The aim of this study was to systematically review and pool the available evidence on the differences in neuromuscular and metabolic responses at LL-RE with different pressure of BFR. Methods: The systematic review and meta-analysis was reported according to PRISMA items. Searches were performed in the following databases: CINAHL, PubMed, Scopus, SPORTDiscus and Web of Science, until June 15, 2021. Randomized or non-randomized experimental studies that analyzed LL-RE, associated with at least two relative BFR pressures [arterial occlusion pressure (AOP)%], on myoelectric activity, fatigue, or metabolic responses were included. Random-effects meta-analyses were performed for MVC torque (fatigue measure) and myoelectric activity. The quality of evidence was assessed using the PEDro scale. Results: Ten studies were included, all of moderate to high methodological quality. For MVC torque, there were no differences in the comparisons between exercise with 40–50% vs. 80–90% AOP. When analyzing the meta-analysis data, the results indicated differences in comparisons in exercise with 15–20% 1 repetition maximum (1RM), with higher restriction pressure evoking greater MVC torque decline (4 interventions, 73 participants; MD = −5.05 Nm [95%CI = −8.09; −2.01], p = 0.001, I 2 = 0%). For myoelectric activity, meta-analyses indicated a difference between exercise with 40% vs. 60% AOP (3 interventions, 38 participants; SMD = 0.47 [95%CI = 0.02; 0.93], p = 0.04, I 2 = 0%), with higher pressure of restriction causing greater myoelectric activity. This result was not identified in the comparisons between 40% vs. 80% AOP. In analysis of studies that adopted pre-defined repetition schemes, differences were found (4 interventions, 52 participants; SMD = 0.58 [95%CI = 0.11; 1.05], p = 0.02, I 2 = 27%). Conclusion: The BFR pressure applied during the LL-RE may affect the magnitude of muscle fatigue and excitability when loads between 15 and 20% of 1RM and predefined repetition protocols (not failure) are prescribed, respectively. Systematic Review Registration: [ http://www.crd.york.ac.uk/prospero ], identifier [CRD42021229345].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".