Passive exercise provides a simultaneous and postexercise executive function benefit
Bibliographic record
Abstract
Introduction: Passive exercise involves limb movement via an external force and is an intervention providing an immediate postexercise executive function (EF) benefit. It is, however, unknown whether EF is improved simultaneous with passive exercise-a salient question given the advent of passive (and active) exercise workstations designed to enhance productivity and wellbeing for individuals engaged in sedentary occupations. Methods: = 23) completed separate 20-min conditions involving active (i.e., via volitional muscle activation) and passive (i.e., via mechanically driven cycle ergometer) cycle ergometry and a non-exercise control condition. EF was assessed prior to (i.e., preintervention), simultaneous with, and immediately after (post-intervention) each condition via the antipointing task. Antipointing involves a goal-directed limb movement mirror-symmetrical to a target and is an ideal tool for the current investigation given that the task is mediated via EF inhibitory control networks that show response-dependent changes following a single bout of exercise. Results and discussion: Results showed that passive exercise produced a simultaneous and post-intervention reduction in antipointing reaction time (RT), whereas active exercise selectively produced a post-intervention-but not simultaneous-RT reduction. Thus, passive and active exercise elicited a postexercise EF benefit; however, only passive exercise produced a simultaneous benefit. That passive-but not active-exercise produced a simultaneous benefit may reflect that the intervention provides the necessary physiological or psychological changes to elicit improved EF efficiency without the associated dual-task cost(s) of volitional muscle activity.
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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) | 0.010 | 0.001 |
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".