Effect of Plyometric vs. Dynamic Weight Training on the Energy Cost of Running
Bibliographic record
Abstract
The purpose of this study is to compare the effects of 2 strength training methods on the energy cost of running (Cr). Thirty-five moderately to well-trained male endurance runners were randomly assigned to either a control group (C) or 2 intervention groups. All groups performed the same endurance-training program during an 8-week period. Intervention groups added a weekly strength training session designed to improve neuromuscular qualities. Sessions were matched for volume and intensity using either plyometric training (PT) or purely concentric contractions with added weight (dynamic weight training [DWT]). We found an interaction between time and group (p < 0.05) and an effect of time (p < 0.01) for Cr. Plyometric training induced a larger decrease of Cr (218 +/- 16 to 203 +/- 13 ml.kg.km) than DWT (207 +/- 15 to 199 +/- 12 ml.kg.km), whereas it remained unchanged in C. Pre-post changes in Cr were correlated with initial Cr (r = -0.57, p < 0.05). Peak vertical jump height (VJHpeak) increased significantly (p < 0.01) for both experimental groups (DWT = 33.4 +/- 6.2 to 34.9 +/- 6.1 cm, PT = 33.3 +/- 4.0 to 35.3 +/- 3.6 cm) but not for C. All groups showed improvements (p < 0.05) in Perf3000 (C = 711 +/- 107 to 690 +/- 109 seconds, DWT = 755 +/- 87 to 724 +/- 77 seconds, PT = 748 +/- 81 to 712 +/- 76 seconds). Plyometric training were more effective than DWT in improving Cr in moderately to well-trained male endurance runners showing that athletes and coaches should include explosive strength training in their practices with a particular attention on plyometric exercises. Future research is needed to establish the origin of this adaptation.
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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) | 0.002 | 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".