Quadriceps effort during squat exercise depends on hip extensor muscle strategy
Bibliographic record
Abstract
Hip extensor strategy, specifically relative contribution of gluteus maximus versus hamstrings, will influence quadriceps effort required during squat exercise, as hamstrings and quadriceps co-contract at the knee. This research examined the effects of hip extensor strategy on quadriceps relative muscular effort (RME) during barbell squat. Inverse dynamics-based torque-driven musculoskeletal models were developed to account for hamstrings co-contraction. Net joint moments were calculated using 3D motion analysis and force platform data. Hamstrings co-contraction was modelled under two assumptions: (1) equivalent gluteus maximus and hamstrings activation (Model 1) and (2) preferential gluteus maximus activation (Model 2). Quadriceps RME, the ratio of quadriceps moment to maximum knee extensor strength, was determined using inverse dynamics only, Model 1 and Model 2. Quadriceps RME was greater in both Models 1 and 2 than inverse dynamics only at barbell loads of 50-90% one repetition maximum. The highest quadriceps RMEs were 120 ± 36% and 87 ± 28% in Models 1 and 2, respectively, which suggests that barbell squats are only feasible using the Model 2 strategy prioritising gluteus maximus versus hamstrings activation. These results indicate that developing strength in both gluteus maximus and quadriceps is essential for lifting heavy loads in squat exercise.
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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 | 0.002 |
| 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) | 0.001 | 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".