Hamstring Myoelectrical Activity During Three Different Kettlebell Swing Exercises
Bibliographic record
Abstract
Del Monte, MJ, Opar, DA, Timmins, RG, Ross, JA, Keogh, JWL, and Lorenzen, C. Hamstring myoelectrical activity during three different kettlebell swing exercises. J Strength Cond Res 34(7): 1953-1958, 2020-Kettlebell exercises have become an increasingly popular form of resistance training and component of lower-body rehabilitative training programs, despite a lack of scientific literature illustrating internal mechanisms and effectiveness of these approaches. Participants (n = 14) performed 3 different styles of kettlebell swings (hip hinge, squat, and double knee extension) and were assessed for medial hamstrings (MHs) and biceps femoris (BF) myoelectrical activity through surface electromyography (sEMG). Bipolar pregelled Ag/AgCl sEMG electrodes (10 mm diameter, 20 mm interelectrode distance) were placed on the participant's dominant limb after correct skin preparation. There was a main effect for swing type (p = 0.004), where the hip hinge swing elicited a greater overall MH and BF sEMG in comparison with the squat swing (mean difference = 3.92; 95% confidence interval [CI] = 1.53-6.32; p = 0.002) and the double knee extension swing (mean difference = 5.32; 95% CI = 0.80-9.83; p = 0.020). Across all swing types, normalized percentage of MH sEMG was significantly higher compared with the BF (mean difference = 9.93; 95% CI = 1.67-18.19; p = 0.022). The hip hinge kettlebell swing produced the greatest amount of hamstring sEMG for the 3 styles of kettlebell swings assessed. These findings have implications for the application of kettlebell swing exercises in strength and conditioning, injury prevention, and rehabilitation programs.
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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".