Sex Difference in Lower-limb Electromyography and Kinematics when Using Resistance Bands during a Barbell Back Squat
Bibliographic record
Abstract
The aim of this study was to compare the muscle activity of the gluteus medius (GMe), gluteus maximus (GMa), biceps femoris (BF), vastus lateralis (VL), vastus medialis (VM) and erector spinae (ES) as well as medial knee displacement (MKD) while using varying stiffness resistance bands (red: 1.68 kg; black: 3.31 kg; gold: 6.44 kg) during a barbell back squat (BBS) among males and females. A total of 23 (females: 11) resistance trained people were recruited for this study. Muscle activity was measured using electromyography, and motion capture cameras tracked lower-limb kinematics and MKD. Three resistance bands were placed at the distal end of the femur while performing a BBS at their 85% repetition maximum (RM). Parametric and non-parametric statistical analyses were conducted with the alpha level of 0.05. The gold resistance band resulted in a smaller knee-width-index value (i.e., greater MKD) compared to other bands (p < 0.01). Males exhibited less MKD compared to females during the BBS for each resistance band (p = 0.04). Males produced greater VL activity when using the black and gold resistance bands during the BBS (p = 0.03). When using a gold resistance band, the GMe muscle activation was higher compared to other resistance bands (p < 0.01). VM muscle activity was reduced when using a gold resistance band compared to no band condition (p < 0.01). BF (p = 0.39) and ES (p = 0.88) muscle activity did not change when using different resistance bands. As a result, females may be at a biomechanical disadvantage when using resistance bands compared to males while performing the BBS hindering them from optimal performance.
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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) | 0.002 | 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".