Does Music Support Cognitive Control and Affective Responses During Acute Exercise? An Exploratory Systematic Review and Meta-analysis
Bibliographic record
Abstract
Cognitive control, defined as the allocation of mental resources required for goal-directed behaviour, is crucial for exercise participation as it is involved in regulating negative cognitive and affective responses caused by the demands of exercise. Research on both music and acute exercise separately show engagement of cognitive control processes and affective responses, with low-to-moderate exercise intensities reliably influencing cognitive and affective outcomes (e.g., core affect). However, the combined effects of music and acute exercise on cognitive control and affective outcomes remain underexplored. Accordingly, this review and meta-analysis explores how music influences cognitive control and affective outcomes during acute exercise. 10 studies met the inclusion criteria, with nine providing data for effect size calculations across 21 intervention arms. Meta-analyses revealed significant effects of music on attention allocation (g = 1.05, 95% CI [0.03, 2.07]; p = 0.04), inhibitory control (g = 1.87, 95% CI [0.37, 3.37]; p = 0.01), and core affect (g = 0.86, 95% CI [0.24, 1.48]; p < 0.01). Exercise intensity significantly moderated outcomes (p = 0.036), suggesting that higher intensities diminish the effectiveness of music in elevating cognitive control and affective outcomes during acute exercise. Findings were limited by high heterogeneity (I2 > 97%) across study protocols and outcome measures. Due to the aforementioned heterogeneity, the findings of this review must be interpreted cautiously.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".