Effects of Short-Term Concurrent Training Cessation on the Energy Cost of Running and Neuromuscular Performances in Middle-Distance Runners
Bibliographic record
Abstract
Evidence supports the implementation of concurrent strength and running training, within the same mesocycle, to improve performances in middle- and long-distance events. However, very little is known about the effects of concurrent training cessation. The purpose of this investigation was to describe the effects of 4 weeks of explosive strength training cessation after an 8-week concurrent training protocol. Eight runners completed this study, which first included either plyometric (n = 4) or dynamic weight training (n = 4) in addition to the usual running regimen. Explosive strength training was thereafter interrupted for 4 weeks, during which running sessions were maintained. Participants were tested at baseline, after concurrent training and after concurrent training cessation. The results suggest that the energy cost of running improvements observed after the intervention (−5.75%; 95% CI = −8.47 to −3.03) were maintained once explosive strength training was interrupted (−6.31%; 95% CI = −10.30 to −2.32). The results also suggest that neuromuscular performances were maintained after 4 weeks of concurrent training cessation, especially when tests were specific to the training intervention. Furthermore, a 3000m time trial revealed a similar pattern, with improvements after the concurrent mesocycle (−2.40%; 95% CI = −4.65 to −0.16) and after concurrent training cessation (−4.43%; 95% CI = −6.83 to −2.03). Overall, only trivial changes were observed for aerobic endurance and V˙O2peak. Together, these results suggest that short-term explosive strength training cessation might be beneficial and could be considered as a taper strategy for middle-distance runners. However, coaches and athletes must interpret these results cautiously considering the study’s low sample size and the very limited available literature in this domain.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".