Effects of Loading Positions on Lower Limb Biomechanics During Lunge Squat in Men with Different Training Experience
Bibliographic record
Abstract
Background: The lunge exercise is commonly adopted in public fitness programs. However, inadequate training experience and knowledge may result in improper actions, resulting in lowering training efficacy and possibly causing exercise-related diseases. Methods: Twenty-four male fitness trainees (12 novices and 12 experienced seniors) were recruited. Kinematics, kinetics, and muscle activation were measured during forward and backward lunges with different loading positions. Two-way repeated measures ANOVA and one-dimensional statistical parametric mapping (SPM1D) were employed to explore various between training experience across different loading positions and lunge directions. Results: Hip, knee, and ankle ROM and angle peaks were significantly greater in novices than in seniors (P < 0.001). During forward lunges, dumbbells reduced hip moments and stiffness in novices (P < 0.001), while barbells increased ankle moments in seniors (P = 0.022). In the backward lunges, novices showed increased negative power and significant joint instability with dumbbells. Conclusions: Training experience is the significant influence during male lunge exercises. Novices show more stability when using dumbbells instead of barbells for forward lunges. Novices are less stable during backward lunges than more experienced seniors. Overall, novices get a greater benefit of training with dumbbells, while seniors are less likely to hurt their knees and ankles when they use barbells.
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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 | 0.001 |
| 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".