The transfer of strength and power into the stroke biomechanics of young swimmers over a 34‐week period
Bibliographic record
Abstract
Abstract The purpose of this study was to learn the interplay between dry‐land strength and conditioning, and stroke biomechanics in young swimmers, during a 34‐week training programme. Twenty‐seven swimmers (overall: 13.33 ± 0.85 years old; 11 boys: 13.5 ± 0.75 years old; 16 girls: 13.2 ± 0.92 years old) competing at regional‐ and national‐level competitions were evaluated. The swimmers were submitted to a specific in‐water and dry‐land strength training over 34 weeks (and evaluated at three time points: pre‐, mid‐, and post‐test; M1, M2, and M3, respectively). The 100‐m freestyle performance was chosen as the main outcome (i.e. dependent variable). The arm span (AS; anthropometrics), throwing velocity (TV; strength), stroke length (SL), and stroke frequency (SF; kinematics) were selected as independent variables. There was a performance enhancement over time (M1 vs. M3: 68.72 ± 5.57 s, 66.23 ± 5.23 s; Δ = −3.77%; 95% CI: −3.98;−3.56) and an overall improvement of the remaining variables. At M1 and M2, all links between variables presented significant effects ( p < .001), except the TV–SL and the TV–SF path. At M3, all links between variables presented significant effects ( p ≤ .05). Between M1 and M3, the direct effect of the TV to the stroke biomechanics parameters (SL and SF) increased. The model predicted 89%, 88%, and 92% of the performance at M1, M2, and M3, respectively, with a reasonable adjustment (i.e. goodness‐of‐fit M1: χ 2 /df = 3.82; M2: χ 2 /df = 3.08; M3: χ 2 /df = 4.94). These findings show that strength and conditioning parameters have a direct effect on the stroke biomechanics, and the latter one on the swimming performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".