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Record W4307091980 · doi:10.1123/jsep.2021-0330

Is It Really Worth the Effort? Examining the Effects of Mental Fatigue on Physical Activity Effort Discounting

2022· article· en· W4307091980 on OpenAlexaff
Sheereen Harris, Paul W. Stratford, Steven R. Bray

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyMental fatigueDiscountingSocial psychologyDelay discountingApplied psychologyDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

Physical activity (PA) guidelines are informed by epidemiological evidence but do not account for people's motivation for exerting physical effort. Previous research has shown that people are less motivated to engage in moderate- to vigorous-intensity PA when fatigued. In a two-study series, we investigated how intensity and duration affected people's willingness to engage in PA using an effort-discounting paradigm. A secondary purpose was to examine whether effort discounting was affected by mental fatigue. Both studies revealed a significant Intensity × Duration interaction demonstrating a reduced willingness to engage in PA of higher intensities across increasing duration levels. Study 1 demonstrated greater effort discounting for vigorous-intensity PA with increasing mental fatigue; however, this effect was not observed in Study 2. Findings provide novel insight toward people's motivation for engaging in PA based on the properties of the task, and some evidence suggesting feelings of fatigue may also influence motivation to exert physical effort.

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)0.005
metaresearch head score (Gemma)0.023
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.411
Teacher spread0.345 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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