Do Activities Performed within the Intra-Contrast Rest Interval Affect Neuromuscular Performance during Complex-Contrast Training Protocols?
Bibliographic record
Abstract
The aim of this study was to analyze the acute effects of including different exercises within the intra-contrast rest interval (ICRI) of a complex-contrast training (CCT) session. Seventeen recreationally active males completed three different CCT protocols. Programs consisted of a contrast pair combining a moderate-intensity conditioning activity (i.e., a back squat) with a lower-body high-velocity exercise (i.e., a vertical jump) and only differed in the activities performed during the ICRI: 1) passive recovery (CCTPASS); 2) a mobility exercise (CCTMOB); and 3) an upper-body high-intensity strength exercise (i.e., a bench press) (CCTSTR). Countermovement jump and bench press throw metrics were evaluated at baseline and after each set during the workout. The rate of perceived exertion was recorded post-session. Non-significant differences in performance were found between CCTPASS, CCTMOB and CCTSTR throughout the session. Significant declines (p < 0.05) were observed for CMJ peak power in the last 2–3 repetitions of each set, irrespective of the protocol. CCTSTR was perceived as more intense than CCTPASS and CCTMOB (p < 0.05). From a neuromuscular performance perspective, including activities during the ICRI (mobility drills or high-intensity strength exercises) may be a suitable strategy to optimize CCT prescription since the acute responses were similar to those found with passive rest periods. Finally, prescribing a lower number of repetitions per set is recommended to attenuate mechanical performance impairment during CCT protocols, irrespective of the activities completed within the ICRI.
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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) | 0.001 | 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".