Balance and Lower Limb Muscle Activation Between in-Line and Traditional Lunge Exercises
Bibliographic record
Abstract
In-line and traditional lunge exercises present differences in technique as lower limb positioning (anterioposterior), and medio-lateral (ML) balance may differentially affect primary and stabilizer muscles. The purposes of this study were to examine ML balance and muscle activation in anterior and posterior leg positions between in-line and traditional lunge exercises. Fifteen young, healthy, resistance-trained men (25 ± 5 years) performed 2 different lunge exercises (in-line and traditional) at their 10 repetition maximum in a randomized, counterbalanced fashion. Surface electromyography measured muscle activation of the vastus lateralis, biceps femoris, gluteus maximus, and gluteus medius. ML balance was measured with a Wii Fit Balance Board. The vastus lateralis activity was not significantly different between exercises or leg positions. The biceps femoris activity was not significantly different between exercises, however, it was significantly greater in the anterior compared to the posterior position for the in-line (p = 0.003), and traditional lunge (p < 0.001). The gluteus maximus activity was not significantly different between exercises, however, it was significantly greater in the anterior compared to posterior position for the in-line (p < 0.001) and traditional lunge (p < 0.001). ML balance was significantly greater in the in-line exercise in the anterior limb (p = 0.001). Thus, both in-line and traditional lunge exercises presented similar overall levels of muscle activation, yet the anterior limb generated the highest biceps femoral and gluteus maximus muscle activation when compared to the posterior limb. The in-line lunge presents greater ML balance when compared to the traditional lunge 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 | 0.001 |
| 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".