Effects of Motivational Self-Talk on Endurance and Cognitive Performance in the Heat
Bibliographic record
Abstract
PURPOSE: We tested the effectiveness of a 2-wk motivational self-talk (MST) intervention-specific to heat tolerance-on endurance capacity and cognitive function in the heat. METHODS: Eighteen trained male (n = 14) and female (n = 4) cyclists randomly received 2 wk of MST training (n = 9) or a control regimen (CON, n = 9). The experimental protocol was a PRE/POST design consisting of 30 min of cycling at 60% peak power output (PPO) in the heat (35°C, 50% relative humidity, ~3.0 m·s airflow), a 30-min rest period, followed by a time to exhaustion (TTE) test at 80% PPO, and an identical rest period. Executive function, reaction time, and working memory were tested at baseline and each rest period. Key measures included TTE, speed and accuracy on the cognitive tests, rectal temperature, HR, oxygen consumption, and RPE. RESULTS: Group (MST vs CON) × test (PRE vs POST) × time repeated-measures ANOVA revealed that MST significantly increased TTE from PRE (487 ± 173 s) to POST (679 ± 251 s, P = 0.021) concurrent with a higher terminating rectal temperature (PRE, 38.5°C ± 0.2°C; POST, 38.8°C ± 0.4°C; P = 0.023); no TTE (PRE, 531 ± 178 s; POST, 510 ± 216 s; P = 0.28) or rectal temperature (PRE, 38.4°C ± 0.3°C; POST, 38.4°C ± 0.2°C; P = 1.000) changes were found in CON. MST significantly improved both speed and accuracy for executive function from PRE/POST, with no PRE/POST differences for CON on any cognitive measure. There were no interactions (all P > 0.05) for other key measures. CONCLUSION: Motivational self-talk is effective in altering the internal psychophysiological control of exercise and plays a role in improving endurance capacity and executive function in the heat.
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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 | 0.000 | 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) | 0.002 | 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".