The Influence Of Unilateral Handgrip Training With Blood Flow Restriction On The Cross-education Of Strength
Bibliographic record
Abstract
When one limb is strength trained, strength can increase in the opposite untrained limb. This effect is thought to be greater with higher intensities. However, there is some (albeit scant) evidence for a cross-education effect with low-intensity exercise with blood flow restriction (BFR). It is unclear how this would compare with traditional high intensity exercise. PURPOSE: To examine whether low-intensity training with BFR augments the cross-education of strength compared to exercise without BFR. METHODS: 179 participants (77 males, 102 females; 21.5 ± 3.5 years) completed this 6-week study. A pre-test was performed prior to the start of the first training session and a post visit after the last training session. Participants were randomized into one out of four possible groups. Of the four groups, three included variations of four sets of unilateral isometric handgrip training: 1) low-intensity (30% maximal contraction for two minutes (LI, n = 47), 2) low-intensity plus blood flow restriction (50% arterial occlusion pressure) for two minutes (LI-BFR, n = 41), 3) high-intensity (100% maximal contraction) for five seconds (HI, n = 47), and 4) a time-match, non-exercise control group (CON, n = 44). The training groups visited the laboratory three times a week for six weeks (18 training sessions). A Bayesian ANCOVA was used to determine changes in strength for the trained and untrained limb. The pre-test value was used as the covariate. Data is presented as mean (standard deviation). RESULTS: Strength changed differently between groups in the untrained limb (BF10: 3.35). LI-BFR was the only group that observed a cross-education in strength relative to the CON (BF10: 14.4). The pre to post changes in strength of the untrained limb are as listed: CON: 0.6 (2.9), LI: 0.9 (3.6), LI-BFR: 2.7 (3.3), and HI: 0.8 (3.1) kg. Strength in the trained limb also change differently between groups (BF10: 271147). Changes in the HI [4.8 (2.8) kg] and LI-BFR [2.8 (4.0) kg] groups were greater than that of the CON [0.7 (2.9) kg]. However, the LI group [1.3 (2.8) kg] was not different from CON. CONCLUSION: Contrary to previous work using dynamic contraction, the cross-education effect was not intensity dependent. Cross-education was only observed with LI-BFR, suggesting an intensity independent mechanism with isometric handgrip 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 | 0.001 | 0.001 |
| 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".