Exercise prescription and strategies to promote the cross-education of strength: a scoping review
Bibliographic record
Abstract
This review examines the experimental evidence regarding unilateral resistance training frequency, intensity, the type of training, training volume, and adjuvant therapies on the cross-education of strength. CINAHL, MEDLINE, APA PsycInfo, SPORTDiscus, and Web of Science were systematically searched with gray literature searches and pearling of references thereafter. Experiments were included in the review if they performed a unilateral resistance training intervention that directly compared the dose of a training variable on the cross-education response in healthy or clinical populations following a minimum of two weeks of training. Experiments must have reported maximal strength outcomes for the untrained limb. For each experiment, the study population, intervention methods, the dosage of the training variable being studied, and the outcomes for the untrained, contralateral limb were identified and collectively synthesized. The search returned a total of 912 articles, 57 of which qualified for inclusion. The results show that experimental trials have been conducted on resistance training frequency ( n = 4), intensity ( n = 7), the type of training ( n = 26), training volume ( n = 3), and adjuvant therapies ( n = 17) on the cross-education of strength. This review maps the available evidence regarding exercise design and prescription strategies to promote the cross-education of strength. It appears that traditional resistance training frequencies (i.e., 2–3×/week) at high intensities are effective at promoting cross-education with eccentric muscle actions showing additive benefits. There is experimental evidence that neuromodulatory techniques can augment cross-education when layered with unilateral resistance training versus training alone. Registration identifier (osf.io/9sh5b). The cross-education of strength is moderated by exercise design and prescription in clinical and non-clinical populations. This review synthesizes the available evidence regarding exercise design strategies for unilateral resistance training and provides evidence-based recommendations for the prescription of unilateral training to maximize the cross-education of strength. Greater insights regarding the timing and effectiveness of cross-education interventions in clinical scenarios will strengthen the use of unilateral resistance training for individuals who may benefit from its use.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".