Effect of Squat Depth and Barbell Load on Relative Muscular Effort in Squatting
Bibliographic record
Abstract
Resistance training is used to develop muscular strength and hypertrophy. Large muscle forces, in relation to the muscle's maximum force-generating ability, are required to elicit these adaptations. Previous biomechanical analyses of multi-joint resistance exercises provide estimates of muscle force but not relative muscular effort (RME). The purpose of this investigation was to determine the RME during the squat exercise. Specifically, the effects of barbell load and squat depth on hip extensor, knee extensor, and ankle plantar flexor RME were examined. Ten strength-trained women performed squats (50-90% 1 repetition maximum) in a motion analysis laboratory to determine hip extensor, knee extensor, and ankle plantar flexor net joint moment (NJM). Maximum isometric strength in relation to joint angle for these muscle groups was also determined. Relative muscular effect was determined as the ratio of NJM to maximum voluntary torque matched for joint angle. Barbell load and squat depth had significant interaction effects on hip extensor, knee extensor, and ankle plantar flexor RME (p < 0.05). Knee extensor RME increased with greater squat depth but not barbell load, whereas the opposite was found for the ankle plantar flexors. Both greater squat depth and barbell load increased hip extensor RME. These data suggest that training for the knee extensors can be performed with low relative intensities but require a deep squat depth. Heavier barbell loads are required to train the hip extensors and ankle plantar flexors. In designing resistance training programs with multi-joint exercises, how external factors influence RME of different muscle groups should be considered to meet training objectives.
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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) | 0.002 | 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".