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Record W2021105172 · doi:10.1080/02640410600898087

Effect of different recovery patterns on repeated-sprint ability and neuromuscular responses

2006· article· en· W2021105172 on OpenAlexaff
François Billaut, Fabien A. Basset

Bibliographic record

VenueJournal of Sports Sciences · 2006
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMemorial University of NewfoundlandUniversity of Lethbridge
Fundersnot available
KeywordsSprintRepeated measures designPhysical medicine and rehabilitationMedicinePhysical therapyMathematicsStatistics

Abstract

fetched live from OpenAlex

We examined the effect of recovery pattern on mechanical and neuromuscular responses in active men during three repeated-sprint ability tests consisting of ten 6-s cycling sprints. Within each test, the recovery duration was manipulated: constant, increasing, and decreasing recovery pattern. Maximal voluntary contractions of the knee extensors were performed before and after the repeated-sprint ability tests to assess strength and electromyographic activity [root mean square (RMS)] of the quadriceps muscle. We observed different fatigue patterns for peak and mean power output between recovery patterns, with earlier decrements recorded during the increasing recovery pattern. Total work performed over the ten sprints was also lower in the increasing recovery pattern (43.8 +/- 5.4 kJ; P < 0.05). However, the decreasing recovery pattern induced a greater overall power output decrement across the sprints (-15.8%; P < 0.05), compared with the increasing recovery pattern (-5.1%) but not the constant recovery pattern (-10.1%). The decreasing recovery pattern was also associated with higher post-sprint RMS values (+16.2%; P < 0.05). Therefore, the recovery pattern within successive short sprints may influence repeated-sprint ability, and may lead to greater post-sprint neuromuscular adjustments when recovery intervals decrease between sprints. We conclude that peripheral impairments caused the major differences in repeated-sprint ability between recovery patterns.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.278
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2006
Admission routes1
Has abstractyes

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