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Record W2168222263 · doi:10.1080/026404100365144

Acute effects of intense interval training on running mechanics

2000· article· en· W2168222263 on OpenAlexaff
Margaret H. Collins, David J. Pearsall, Gerald S. Zavorsky, Hamid Bateni, R. Turcotte, David Montgomery

Bibliographic record

VenueJournal of Sports Sciences · 2000
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcGill University
Fundersnot available
KeywordsRunning economyKinematicsMathematicsInterval (graph theory)Animal scienceSimulationPhysicsCombinatoricsMedicineComputer scienceVO2 maxHeart rateInternal medicineBiology

Abstract

fetched live from OpenAlex

The aims of this study were to determine if there are significant kinematic changes in running pattern after intense interval workouts, whether duration of recovery affects running kinematics, and whether changes in running economy are related to changes in running kinematics. Seven highly trained male endurance runners (VO2max = 72.3+/-3.3 ml x kg(-1) x min(-1); mean +/- s) performed three interval running workouts of 10 x 400 m at a speed of 5.94+/-0.19 m x s(-1) (356+/-11.2 m x min(-1)) with a minimum of 4 days recovery between runs. Recovery of 60, 120 or 180 s between each 400 m repetition was assigned at random. Before and after each workout, running economy and several kinematic variables were measured at speeds of 3.33 and 4.47 m x s(-1) (200 and 268 m x min(-1)). Speed was found to have a significant effect on shank angle, knee velocity and stride length (P < 0.05). Correlations between changes pre- and post-test for VO2 (ml x kg(-1) x min(-1)) and several kinematic variables were not significant (P > 0.05) at both speeds. In general, duration of recovery was not found to adversely affect running economy or the kinematic variables assessed, possibly because of intra-individual adaptations to fatigue.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.298
Teacher spread0.272 · 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

Citations37
Published2000
Admission routes1
Has abstractyes

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