Sled Towing: The Optimal Overload for Peak Power Production
Bibliographic record
Abstract
PURPOSE: The effects of different loads on kinematic and kinetic variables during sled towing were investigated with the aim to identify the optimal overload for this specific sprint training. METHODS: Thirteen male sprinters (100-m personal best: 10.91 ± 0.14 s) performed 5 maximal trials over a 20-m distance in the following conditions: unloaded and with loads from 15% to 40% of the athlete's body mass (BM). In these calculations the sled mass and friction were taken into account. Contact and flight times, stride length, horizontal hip velocity (vh), and relative angles of hip, knee, and ankle (at touchdown and takeoff) were measured step by step. In addition, the horizontal force (Fh) and power (Ph) and maximal force (Fh0) and power (Ph0) were calculated. RESULTS: vh, flight time, and step length decreased while contact time increased with increasing load (P < .001). These variables changed significantly also as a function of the step number (P < .01), except between the 2 last steps. No differences were observed in Fh among loads, but Fh was larger in sled towing than in unloaded. Ph was unaffected by load up to +20%BM but decreased with larger loads. Fh0 and Ph0 were achieved at 20%BM. Up to 20%BM, no significant effects on joint angles were observed at touchdown and takeoff, while at loads >30%BM joint angles tended to decrease. CONCLUSION: The 20%BM condition represents the optimal overload for peak power production-at this load sprinters reach their highest power without significant changes in their running technique (eg, joint angles).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".