Cycling Performance Enhancement After Drop Jumps May Be Attributed to Postactivation Potentiation and Increased Anaerobic Capacity
Bibliographic record
Abstract
de Poli, RAB, Boullosa, DA, Malta, ES, Behm, D, Lopes, VHF, Barbieri, FA, and Zagatto, AM. Cycling performance enhancement after drop jumps may be attributed to postactivation potentiation and increased anaerobic capacity. J Strength Cond Res 34(9): 2465-2475, 2020-The study aimed to investigate the effects of drop jumps (DJs) on supramaximal cycling performance, anaerobic capacity (AC), electromyography, and fatigue. Thirty-eight recreational cyclists participated into 3 independent studies. In study 1 (n = 14), neuromuscular fatigue was assessed with the twitch interpolation technique. In study 2 (n = 16), the AC and metabolic contributions were measured with the maximal accumulated oxygen deficit method and the sum of the glycolytic and phosphagen pathways. In study 3 (n = 8), postactivation potentiation (PAP) induced by repeated DJs was evaluated. The DJ protocol was effective for significantly improving cycling performance by +9.8 and +7.4% in studies 1 and 2, respectively (p ≤ 0.05). No differences were observed in electromyography between conditions (p = 0.70); however, the force evoked by a doublet at low (10 Hz) and high frequencies (100 Hz) declined for control (-16.4 and -23.9%) and DJ protocols (-18.6 and -26.9%) (p < 0.01). Force decline was greater in the DJ condition (p < 0.03). Anaerobic capacity and glycolytic pathway contributions were +7.7 and +9.1% higher after DJ protocol (p = 0.01). Peak force during maximal voluntary contraction (+5.6%) and doublet evoked force at 100 Hz (+5.0%) were higher after DJs. The DJ protocol induced PAP, improved supramaximal cycling performance, and increased AC despite higher peripheral fatigue.
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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) | 0.003 | 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".