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Record W2114810591 · doi:10.1123/jsep.32.2.154

Affect and Self-Efficacy Responses During Moderate-Intensity Exercise Among Low-Active Women: The Effect of Cognitive Appraisal

2010· article· en· W2114810591 on OpenAlexaff
Amy Welch, Angie Hulley, Mark R. Beauchamp

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAffect (linguistics)PsychologyCycle ergometerSelf-efficacyPhysical therapyCognitionMultilevel modelExercise intensityVentilatory thresholdMultilevel modellingPhysical medicine and rehabilitationDevelopmental psychologyInternal medicineMedicineVO2 maxSocial psychologyHeart rateBlood pressurePsychiatry

Abstract

fetched live from OpenAlex

To investigate the relationship between cognitive and affective responses during acute exercise, 24 low-active females completed two 30-min bouts of cycle ergometer exercise at 90% of the ventilatory threshold. In one condition participants had full knowledge of the exercise duration (KD); in the other, exercise duration was unknown (UD). Affect and self-efficacy were measured before and every 3 min during exercise, and affect was also measured postexercise. Affect declined throughout the first half of both conditions, and continued its decline until the end of the UD condition, when a rebound effect was observed. Self-efficacy during exercise displayed a similar pattern. Hierarchical regression analyses demonstrated that during-exercise self-efficacy was a stronger predictor of during-exercise affect than preexercise self-efficacy, and that this relationship was strongest at the end of exercise when duration was unknown. These results indicate that repetitive cognitive appraisal of self and the task could impact the exercise experiences of low-active women during the adoption phase of an exercise program.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.370
Teacher spread0.353 · 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

Citations55
Published2010
Admission routes1
Has abstractyes

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