Effects of tapering on neuromuscular and metabolic fitness in team sports: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Purpose: To assess the effects of a taper strategy on neuromuscular and metabolic fitness in team sport athletes, through a systematic review and meta‐analysis. Method: To be included in this meta‐analysis, studies had to involve competitive team sport athletes and a tapering intervention providing details about the procedures used to decrease the training load, as well as competition or field‐based criterion performance and all necessary data to calculate effect sizes. Four databases were searched according to these criteria, which led to the identification of 895 potential studies and the subsequent inclusion of 14 articles. Independent variables were training intensity, volume and frequency, as well as the pattern of taper and its duration. The dependent variable was performance obtained in various neuromuscular and metabolic tests. Results: There was limited evidence of a moderate taper‐induced improvement in repeated sprint ability (Standardized Mean Difference (SMD) (95%IC;I 2 ) = 0.41 (0.26–0.55;0%)) and moderate evidence of a moderate increase in maximal power (SMD (95%IC;I 2 ) = 0.44 (0.32–0.56;15%)), change of direction speed (SMD (95%IC;I 2 ) = 0.38 (0.15–0.60;28%)) and maximal oxygen uptake (SMD (95%IC;I 2 ) = 0.76 (0.43–1.09;37%)). Conclusion: Tapering is an effective training strategy to improve maximal power, maximal oxygen uptake, repeated sprint ability and change of direction speed in team sports. However, the literature lacks studies using various tapering strategies to compare their effectiveness and make evidence‐based recommendations. Future original studies should focus on this major issue.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".