Executive functioning following an acute bout of cardiovascular exercise: Does a dose-response relationship exist?
Bibliographic record
Abstract
Although there is an abundance of research investigating the effects of exercise on cognition, few studies have investigated acute effects of exercise on performance of tasks involving executive function. Furthermore, dose-response issues involving different exercise intensities and multiple tests of executive function have received little attention. The purpose of the present study was to investigate the effects of varying intensities of aerobic exercise on executive function test performance over a 38-minute follow-up period. University students (N=88) completed baseline measures of executive function (stop-signal task[SST] and Stroop task[ST]) and a graded cardiovascular exercise test on Visit 1. On Visit 2, participants were stratified by gender and fitness level and randomized to one of four conditions: high-intensity interval (HIIT), high, moderate or low-intensity steady-state exercise performed on a cycle ergometer. The ST and SST were performed immediately following exercise and again at 10- and 30-minutes post-exercise. Immediately following exercise, ST response times were faster for the high and moderate intensity exercise conditions in comparison to low-intensity (p .05). The present outcomes demonstrate beneficial effects of exercise, regardless of intensity, for up to 38 minutes post-exercise. However, no clear dose-response relationship between exercise intensity and executive function performance was evident. Future research should focus on mechanisms that would account for these effects and factors that support enhanced executive function performance with exercise training.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".