Unilateral High-load Resistance Training Influences Strength Changes In The Contralateral Arm Undergoing Low-load Training
Bibliographic record
Abstract
Within-subject training models, whereby researchers apply an exercise condition to one limb, and a separate exercise condition to the opposing limb, have become routine within the literature. Although training one limb can influence strength in the opposite untrained limb, it is unknown whether the cross-education effect is still present when the arm receiving this transfer of strength is also engaging in some form of training. PURPOSE: To determine if unilateral high-load training influences strength adaptations within the contralateral limb engaging in low-load training. METHODS: 116 participants (18-35 yrs) were randomized to one of three intervention groups, and completed 18 training sessions involving isotonic elbow flexion exercise. Group 1 (n = 40) only trained their dominant arm, with a one-repetition maximum (1RM) test (maximum 5 attempts), followed by 4 sets of traditional exercise at an 8-12 RM. Group 2 (n = 39) completed the same training as Group 1 in their dominant arm, whilst the non-dominant arm completed 4 sets of low-load exercise (30-40 RM). Group 3 (n = 37) trained their non-dominant arm only, performing the same low-load exercise as Group 2. Participants were compared for changes in muscle thickness and isotonic elbow flexion 1RM. Emphasis was placed on the non-dominant arm. RESULTS: Groups 1 [Δ 1.5 (1.1, 1.9) kg; untrained arm] and 2 [Δ1.1 (0.6, 1.4) kg; low-load arm with high load on opposite arm] presented the greatest changes in non-dominant arm strength, as compared to Group 3 ([Δ 0.3 (-0.1, 0.76) kg; low-load only]. Only the arms being directly trained saw changes in muscle thickness, when compared to the untrained limbs (~0.25 cm increase). These findings indicate that unilateral high-load training does confer a cross-education effect. Furthermore, this cross-education effect was still detected within Group 2, despite the contralateral limb opposing the high-load condition engaging in low-load exercise. CONCLUSION: Unilateral high-load elbow flexion exercise resulted in a cross-education effect. This effect also appears to explain the strength changes in the contralateral arm that was completing low-load exercise. These results suggest that within-subject training models can be utilized if measuring changes in muscle size, but are problematic when measuring changes in muscle strength.
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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 | 0.001 |
| 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) | 0.003 | 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".