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Record W2170136558 · doi:10.1123/jsep.2013-0115

Fitness Level Moderates Executive Control Disruption During Exercise Regardless of Age

2014· article· en· W2170136558 on OpenAlexafffund
Véronique Labelle, Laurent Bosquet, Saïd Mekary, Thien Tuong Minh Vu, Mark Smilovitch, Louis Bherer

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsConcordia UniversityUniversité de MontréalUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsStroop effectPsychologyCognitionTask (project management)Executive functionsPhysical fitnessPhysical medicine and rehabilitationDevelopmental psychologyPhysical therapyMedicineNeuroscience

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the effects of exercise intensity, age, and fitness levels on executive and nonexecutive cognitive tasks during exercise. Participants completed a computerized modified-Stroop task (including denomination, inhibition, and switching conditions) while pedaling on a cycle ergometer at 40%, 60%, and 80% of peak power output (PPO). We showed that a bout of moderate-intensity (60% PPO) to high-intensity (80% PPO) exercise was associated with deleterious performance in the executive component of the computerized modified-Stroop task (i.e., switching condition), especially in lower-fit individuals (p < .01). Age did not have an effect on the relationship between acute cardiovascular exercise and cognition. Acute exercise can momentarily impair executive control equivalently in younger and older adults, but individual's fitness level moderates this relation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.289
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes2
Has abstractyes

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