The Use of Elastic Resistance Bands to Reduce Dynamic Knee Valgus in Squat-Based Movements: A Narrative Review
Bibliographic record
Abstract
An elastic band wrapped around the distal thighs has recently been proposed as a method for reducing dynamic knee valgus (medial movement of the knee joint in the frontal/coronal plane) while performing squats. The rationale behind this technique is that, by using an external force to pull the knees into further knee valgus, the band both exaggerates the pre-existing movement and provides additional local proprioceptive input, cueing individuals to adjust their knee alignment. If these mechanisms are true, then elastic bands might indeed reduce dynamic knee valgus, which could be promising for use in injury prevention as excessive knee valgus may be associated with a greater risk of sustaining an ACL rupture and/or other knee injuries. Due to this possibility, certain athletic populations have already adopted the use of elastic bands for training and/or rehab, despite a limited number of studies showing beneficial findings. The purpose of this narrative review is to examine current literature that has assessed lower limb muscle activity and/or lower limb kinematics performance on squat-based movements with or without an elastic band(s). Importantly, this paper will also discuss the key limitations that exist in this area, propose suggestions for future research directions, and provide recommendations for training implementations. Level of Evidence: 5.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".