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Record W2032527238 · doi:10.1139/apnm-2014-0442

Self-pacing increases critical power and improves performance during severe-intensity exercise

2015· article· en· W2032527238 on OpenAlexvenueno aff
Matthew I. Black, Andrew M. Jones, Stephen J. Bailey, Anni Vanhatalo

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
Fundersnot available
KeywordsIntensity (physics)Power (physics)Environmental sciencePhysicsOpticsThermodynamics

Abstract

fetched live from OpenAlex

The parameters of the power-duration relationship for severe-intensity exercise (i.e., the critical power (CP) and the curvature constant (W')) are related to the kinetics of pulmonary O2 uptake, which may be altered by pacing strategy. We tested the hypothesis that the CP would be higher when derived from a series of self-paced time-trials (TT) than when derived from the conventional series of constant work-rate (CWR) exercise tests. Ten male subjects (age, 21.5 ± 1.9 years; mass, 75.2 ± 11.5 kg) completed 3-4 CWR and 3-4 TT prediction trial protocols on a cycle ergometer for the determination of the CP and W'. The CP derived from the TT protocol (265 ± 44 W) was greater (P < 0.05) than the CP derived from the CWR protocol (250 ± 47 W), while the W' was not different between protocols (TT: 18.1 ± 5.7 kJ, CWR: 20.6 ± 7.4 kJ, P > 0.05). The mean response time of pulmonary O2 uptake was shorter during the TTs than the CWR trials (TT: 34 ± 16, CWR: 39 ± 19 s, P < 0.05). The CP was correlated with the total O2 consumed in the first 60 s across both protocols (r = 0.88, P < 0.05, n = 20). These results suggest that in comparison with the conventional CWR exercise protocol, a self-selected pacing strategy enhances CP and improves severe-intensity exercise performance. The greater CP during TT compared with CWR exercise has important implications for performance prediction, suggesting that TT completion times may be overestimated by CP and W' parameters derived from CWR protocols.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.773

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 head0.009
GPT teacher head0.233
Teacher spread0.225 · 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 designBench or experimental
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

Citations78
Published2015
Admission routes1
Has abstractyes

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