The Effect of Stance Width and Anthropometrics on Joint Range of Motion in the Lower Extremities during a Back Squat
Bibliographic record
Abstract
The purpose of this study was to assess whether changing the stance width has an effect on the range of motion of hip flexion, knee flexion, and ankle dorsiflexion during an unloaded back squat, and whether these joint movements are affected by anthropometric differences. Thirty-two healthy, young adults performed unloaded back squats at three different stance widths, normalized to pelvic width. Joint angles were assessed using electromagnetic motion capture sensors on the sacrum, and thigh, shank and foot of the dominant leg. ANOVA comparison of joint angles for the three stance widths, at 10° intervals of thigh orientation during the squat, indicated that joint angles tended to be larger when stance width was narrower, with the most significant effects on ankle dorsiflexion. A greater trunk/thigh length ratio (relatively long trunk) also tended to be associated with lower ankle and knee angles, while a greater thigh/shank length ratio (relatively long thigh) tended to be associated with higher ankle and knee angles, for the two narrower stance widths. The most practical implication of our findings is that individuals with limited ankle dorsiflexion, or with particularly long legs / thighs, may benefit from a wider stance width when squatting.
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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) | 0.001 | 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".