Moderate Load Resisted Sprints Do Not Improve Subsequent Sprint Performance in Varsity-Level Sprinters
Bibliographic record
Abstract
ABSTRACT: Thompson, K, Whinton, AK, Ferth, S, Spriet, LL, and Burr, JF. Moderate load resisted sprints do not improve subsequent sprint performance in varsity-level sprinters. J Strength Cond Res 35(1): 72-77, 2021-Resisted sprint training (RST) is commonly used for performance enhancement in athletics and team sports to develop acceleration ability. Evidence suggests that RST may be effective as a short-term intervention to improve successive sprints. Although these improvements have been measured in team sport athletes, limited research has considered the acute effects of RST training in sprint-trained athletes. Therefore, the aim of the current study was to determine whether performing RST with varsity-level sprinters using sled-equivalent resistive loads of ∼45% body mass results in a potentiation effect, leading to improvements in subsequent maximal sprint performance over 0-5 m and 0-20 m. Competitive sprinters (n = 20) were randomly assigned to perform a pre/post maximal 20-m sprint separated by either 3 resisted (RST group) or unresisted (URS group) sprints. The RST or URS protocol was performed on 4 occasions separated by at least 7 days. No significant differences were observed between the RST and URS groups comparing changes in sprint times over 0-5 m (URS Δ <0.01 ± 0.03 seconds, RST Δ <0.01 ± 0.03 seconds) and 0-20 m (URS Δ 0.013 ± 0.04 seconds, RST Δ <0.01 ± 0.04 seconds). We conclude that resisted sprints using sled-equivalent loads of 45% body mass are ineffective at inducing a potentiating effect on subsequent sprint performance in varsity-level sprinters. In this population of trained athletes, greater loads may be necessary to induce a potentiating effect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".