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Record W2409511234 · doi:10.1055/s-0042-107251

Mechanical, Metabolic and Perceptual Response during Sprint Training

2016· article· en· W2409511234 on OpenAlexfundno aff
Pedro Jiménez‐Reyes, Fernando Pareja‐Blanco, Víctor Cuadrado‐Peñafiel, J. Párraga, Juan José González‐Badillo

Bibliographic record

VenueInternational Journal of Sports Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersMcGill University
KeywordsSprintBlood lactateRating of perceived exertionPhysical therapyInterval trainingJumpPerceived exertionMedicineMathematicsAnimal sciencePhysical medicine and rehabilitationInternal medicineHeart ratePhysicsBlood pressureBiology

Abstract

fetched live from OpenAlex

This study aimed to analyze perceptual, metabolic and mechanical responses to sprint training sessions. 9 male high-level sprinters performed 40 m running sprints up to a loss of 3% in speed, with 4 min rests between sets. Perceptual (rating of perceived exertion, RPE), mechanical (speed and countermovement jump height loss) and metabolic (blood lactate and ammonia) parameters were measured pre-exercise and after each sprint was performed. Relationships between the variables were calculated with a 90% confidence interval. Jump height loss showed almost perfect relationships with both blood lactate (r=0.96 (0.95 to 0.97)) and ammonia (r=0.95 (0.94 to 0.95)), whereas speed loss, number of sprints performed and RPE values showed large-very large relationships with blood lactate and ammonia. Furthermore, an almost perfect curvilinear relationship was observed between lactate and ammonia concentrations (R(2)=0.96 (0.95 to 0.97)). These results suggest that countermovement jump (CMJ) height can be used to quantify the fatigue induced during a typical sprint training session, and may prove a useful tool to facilitate individualized load monitoring. The results indicate that the CMJ is a better monitor of metabolic fatigue than traditional measures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score1.000

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)0.0010.000

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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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.

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

Citations38
Published2016
Admission routes1
Has abstractyes

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