Aerobic Exercise Intensity: A Dose-Response Effect on Motor Adaptation and Learning
Bibliographic record
Abstract
ABSTRACT Acute aerobic exercise (AEX) can enhance motor learning. While AEX intensity likely plays a key role, there is mixed evidence for AEX-enhanced motor skill acquisition and learning across a spectrum of exercise intensities. This may stem, in part, from inconsistent AEX parameters (i.e., intensity, structure, and duration) employed within and across studies. Additionally, evidence suggests that AEX can enhance a specific form of motor learning, namely motor adaptation. Moderate- and high-intensity AEX can increase motor adaptation, but evidence remains limited and inconsistent. Hence, the impact of AEX intensity on motor adaptation remains unclear. Here, we investigated the influence of AEX intensity on motor adaptation, while controlling for AEX structure and duration. Eighty young adults were assigned to four cycling AEX/Rest groups (n=20/group): 20 min of light (LIIT), moderate (MIIT), or high (HIIT) intensity interval training, or Rest (control). AEX consisted of four 3-min cycling intervals (LIIT, 35% heart rate reserve [HRR]; MIIT, 55%HRR; HIIT, 80%HRR) and 2-min active recovery (25%HRR). Participants practiced a visuomotor rotation task immediately after AEX/Rest (adaptation) and at a no-AEX 24 h retention test (motor learning). We found that: (1) all AEX intensities enhanced motor learning compared to Rest, and (2) HIIT enhanced motor adaptation and learning to the greatest extent, followed by MIIT then LIIT. This is the first study to demonstrate a dose-response effect of AEX intensity on motor adaptation and learning. Our results highlight the importance of considering intensity when prescribing AEX in sports and clinical contexts to promote motor learning.
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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 | 0.000 |
| 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) | 0.003 | 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".