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Record W3040666332 · doi:10.1055/a-1179-8326

The Effects of Performing Mental Exertion during Cycling Exercise on Fatigue Indices

2020· article· en· W3040666332 on OpenAlexaff
Hamidreza Barzegarpoor, Hamid Amoozi, Hamid Rajabi, Duane C. Button, Rana Fayazmilani

Bibliographic record

VenueInternational Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHeart rateExertionCyclingPerceived exertionPsychologyMedicinePhysical therapyInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Abstract This study investigated the effect of performing prolonged mental exertion during submaximal cycling exercise on exercise tolerance and fatigue. Participants performed 5 experimental sessions. Session 1: determination of cycling peak power output. Sessions 2 and 3: cycling to exhaustion at 65% peak power output with mental exertion or watching a movie. Sessions 4 and 5: cycling for 45 min at 65% peak power output with mental exertion or while watching a movie. During sessions 2–5, rate of perceived exertion and heart rate were recorded while cycling and cortisol and prolactin concentrations, psychomotor vigilance task performance, and maximal voluntary contraction were measured pre-and post-sessions. During sessions 2 and 3, time to exhaustion was reduced (p<0.01) and rate of perceived exertion was increased (p<0.01) in session 2 compared to 3. Cortisol, prolactin and heart rate increased and psychomotor vigilance task and maximal voluntary contraction decreased from pre-to post-sessions with no difference between sessions. Cortisol, prolactin and rate of perceived exertion were higher (p<0.03) in session 4 than 5. Heart rate increased and maximal voluntary contraction decreased from pre-to post-sessions with no difference between sessions. Prolonged mental exertion during cycling exercise reduces exercise tolerance, which appears to be mediated psychologically rather than physiologically.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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