Mechanical Energy Expenditure at Lumbar Spine and Lower Extremity Joints During the Single-Leg Squat Is Affected by the Nonstance Foot Position
Bibliographic record
Abstract
ABSTRACT: Hirsch, SM, Chapman, CJ, Frost, DM, and Beach, TAC. Mechanical energy expenditure at lumbar spine and lower extremity joints during the single-leg squat is affected by the nonstance foot position. J Strength Cond Res 36(9): 2417-2426, 2022-Previous research has shown that discrete kinematic and kinetic quantities during bodyweight single-leg squat (SLS) movements are affected by elevated foot positioning and sex of the performer, but generalizations are limited by the high-dimensional data structure reported. Using a 3D inverse dynamical linked-segment model, we quantified mechanical energy expenditure (MEE) at each joint in the kinetic chain, the total MEE (sum of MEE across aforesaid joints), and the relative contribution of each joint to total MEE during SLSs performed with elevated foot positioned beside stance leg (SLS-Side), and in-front of (SLS-Front) and behind (SLS-Back) the body. Total MEE differed between SLS variations ( p = 0.002), with the least amount observed in the SLS-Back (effect size [ES] = 0.066-0.069). Approximately 50% of total MEE was contributed by the knee joint in each SLS variation, whereas MEE at the ankle, hip, and lumbar spine (in absolute and relative terms) varied complexly as a function of the elevated foot position. Total MEE ( p = 0.0192, ES = 0.852) and the absolute MEE at the knee and spine was greater in men across the SLS variations performed ( p = 0.025-0.036, ES = 0.715-0.766), but only the lumbar spine contribution to total MEE was larger in men across all SLS variations ( p = 0.045, ES = 0.607). Otherwise, there were no other sex-specific responses observed. Biomechanically, SLS movements are generally "knee-dominant," but changing elevated foot position effectively redistributes MEE among other joints in the linkage. Consistent with the previous conclusions reached based on discrete kinematic and kinetic data, not all SLSs are equal.
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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".