A Warm-Up Routine That Incorporates a Plyometric Protocol Potentiates the Force-Generating Capacity of the Quadriceps Muscles
Bibliographic record
Abstract
Johnson, M, Baudin, P, Ley, AL, and Collins, DF. A warm-up routine that incorporates a plyometric protocol potentiates the force-generating capacity of the quadriceps muscles. J Strength Cond Res 33(2): 380-389, 2019-This study was designed to investigate whether a warm-up routine that incorporates drop jumps, induces post-activation potentiation (PAP), and if so, assess the magnitude and time course of the induced PAP. Participants performed a standard warm-up that incorporated either drop jumps (plyometric protocol) or a low-paced walk (control protocol). Post-activation potentiation was assessed by changes in electrically evoked isometric muscle twitches recorded throughout both protocols. The plyometric protocol increased peak twitch torque (PTT), rate of torque development (RTD), and impulse significantly (by 23, 39, and 46%, respectively) with no change in the amplitude of simultaneously evoked M-waves, indicating that the augmented torque was due to PAP. These increases returned to baseline within 6 minutes, and PTT and RTD fell below baseline values at 11-16 minutes after the drop jumps. Peak twitch torque, RTD, and impulse decreased significantly after the standard warm-up. These results provide evidence that drop jumps induce PAP, markedly enhancing the force-generating capacity of the muscle. By contrast, the standard warm-up did not potentiate, but rather reduced, the force-generating capacity of the muscle. We suggest that drop jumps be incorporated into warm-up routines directly before athletic performance to maximize the force-generating capacity of muscle.
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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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".