Effects of mental fatigue on exercise intentions and behavior
Bibliographic record
Abstract
Background: Exerting cognitive control results in mental fatigue, which is associated with impaired performance during physical endurance tasks. However, there has been little research on the effects of mental fatigue on people's perceptions or behaviors involving lifestyle or recreational exercise. Purpose: The purpose of this study was to examine the effect of mental fatigue on intended physical exertion and exercise performance reflective of current physical activity guidelines. Methods: Using a counterbalanced design, participants completed two 50-minute experimental manipulations (high vs. low cognitive control exertion) before exercising at a self-selected intensity for 30 minutes. At Visit 1, participants performed a graded exercise task to gain familiarity with a range of exercise intensities and rating perceived exertion (RPE) while exercising. At Visits 2 and 3, participants rated their intended RPE for the exercise session, performed the experimental manipulations, re-rated their intended RPE, and then completed 30-minutes of exercise on a cycle ergometer. Total work performed while exercising was recorded for each session. Results: Compared to the low cognitive control condition, the high cognitive control manipulation resulted in significantly greater mental fatigue (d = .73), significantly greater reductions in intended RPE (Mean difference = -0.62) and significantly less total work (-12.7 kJ) performed during the exercise session. Conclusions: Mental fatigue alters the amount of physical effort people are willing to invest in an exercise workout and follow through with those intentions by doing less work. These are the first results showing people may deliberately adjust their physical effort to cope with mental fatigue.Acknowledgments: SSHRC; McMaster SSHRC Explore Grant
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".