The Effects of Performing Mental Exertion during Cycling Exercise on Fatigue Indices
Bibliographic record
Abstract
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".