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Record W2156680630 · doi:10.1139/h2012-083

Extremely short duration high-intensity training substantially improves endurance performance in triathletes

2012· article· en· W2156680630 on OpenAlexvenueno aff
John Jakeman, Simon Adamson, John Babraj

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2012
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsTime trialBlood lactateMedicineRegimenAerobic exercisePhysical therapyAnimal scienceHeart rateInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

High-intensity training (HIT) involving 30-s sprints is an effective training regimen to improve aerobic performance. We tested whether 6-s HITs can improve aerobic performance in triathletes. Six subelite triathletes (age, 40 ± 9 years; weight, 86 ± 11 kg; body mass index, 26 ± 3 kg·m⁻²) took part in cycle HIT and 6 endurance-trained subelite athletes (age, 36 ± 9 years; weight, 82 ± 11 kg; BMI, 26 ± 3 kg·m⁻²) maintained their normal training routine. Before and after 2 weeks of HIT, involving 10 × 6-s sprints or normal activity, participants performed a self-paced 10-km time trial and a time to exhaustion test on a cycle ergometer. Finger prick blood samples were taken throughout the time to exhaustion test to determine blood lactate concentration. Two weeks of HIT resulted in a 10% decrease in self-paced 10-km time trial (p = 0.03) but no significant change in time to exhaustion. The time taken to reach onset of blood lactate accumulation (OBLA, defined as the point where blood lactate reaches 4 mmol·L⁻¹) was significantly increased following 2 weeks of HIT (p = 0.003). The change in time trial performance was correlated to the change in time taken to reach OBLA (R² = 0.63; p = 0.001). We concluded that a very short duration HIT is a very effective training regimen to improve aerobic performance in subelite triathletes and this is associated with a delay in blood lactate build-up.

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)0.000
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 score0.950
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.253
Teacher spread0.221 · 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

Citations40
Published2012
Admission routes1
Has abstractyes

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