Ten Minutes of Dynamic Stretching Is Sufficient to Potentiate Vertical Jump Performance Characteristics
Bibliographic record
Abstract
The current literature recommends dynamic rather than static stretching for the athletic warm-up. Dynamic stretching and various conditioning stimuli are used to induce potentiation in subsequent athletic performance. However, it is unknown as to which type of activity in conjunction with dynamic stretching within a warm-up provides the optimal potentiation of vertical jump performance. It was the objective of the study to examine the possible potentiating effect of various types of conditioning stimuli with dynamic stretching. Twenty athletes participated in 6 protocols. All the experimental protocols included 10 minutes of dynamic stretching. After the dynamic stretching, the subjects performed a (a) concentric (DS/CON): 3 sets of 3 repetition maximum deadlift exercise; (b) isometric (DS/ISOM): 3 sets of 3-second maximum voluntary contraction back squats; (c) plyometric (DS/PLYO): 3 sets of 3 tuck jumps; (d) eccentric (DS/ECC): 3 modified drop jumps; (e) dynamic stretching only (DS), and (f) control protocol (CON). Before the intervention and at recovery periods of 15 seconds, 4, 8, 12, 16, and 20 minutes, the participants performed 1-2 maximal countermovement jumps. The DS and DS/CON protocols generally had a 95-99% likelihood of exceeding the smallest worthwhile change for vertical jump height, peak power, velocity and force. However, the addition of the deadlift to the DS did not augment the potentiating effect. Time-to-peak potentiation was variable between individuals but was most consistent between 3 and 5 minutes. Thus, the volume and the intensity associated with 10 minutes of dynamic stretching were sufficient to provide the potentiation of vertical jump characteristics. Additional conditioning activities may promote fatigue processes, which do not permit further potentiation.
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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) | 0.000 | 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) | 0.004 | 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".