Effects of Co-Contraction Training on Neuromuscular Outcomes of Elbow Flexors and Extensors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Co-contraction training has been proposed to improve muscle strength in the absence of external equipment, yet it is needed to elucidate the effects of co-contraction training and its applicability. Thus, we synthesized the effects of co-contraction training on elbow muscle strength, myoelectric activity, and muscle thickness. We searched papers from MEDLINE via PubMed, Web of Science, Scopus and Embase databases. The inclusion criteria were studies comprising adults between 18-64 years old; investigating chronic effects of co-contraction training on elbow muscles; comparing pre- and post-intervention or control values; presenting any of the outcomes; randomized, quasi-experimental, pre- and post-design; in English. Seven studies met the inclusion criteria. We performed a systematic review and meta-analysis following PRISMA recommendations. We used the revised RoB 2, ROBINS-I to verify the level of evidence. We also included a questionnaire for biomechanical studies and GRADE analysis. We extracted data independently by two investigators, considering the characteristics of study, participants and training, outcomes, and results. We calculated the effect sizes for each outcome. The analysis was carried out by combining and dividing flexors and extensors in a subgroup analysis. Comparing the experimental vs. control group, our results showed that cocontraction training increased isometric strength (SMD=0.51 [0.19, 0.83]) and agonist myoelectric activity (SMD=0.54 [0.25, 0.83]). Comparing pre- vs. post-training, co-contraction training also improved isometric strength (SMD=1.28 [0.75, 1.81]); concentric elbow extensor strength (SMD=0.64 [0.01, 1.26]); and myoelectric activity (SMD=0.46 [0.18, 0.73]). No effect was observed for muscle thickness. The co-contraction training improves muscle performance without morphological changes.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".