Brief Physical Activity Selectively Modulates the Performance of Serial Subtract 7 in Young Adults – A Wearable Sensor-based, Randomized, Control Study
Bibliographic record
Abstract
Abstract OBJECTIVE This study explores the effects of physical activities on cognitive performance in healthy subjects, specifically evaluating Serial Subtract 7 Test (SST) performance during a cognitive-stepping dual task influenced by the 6-Minute Walking Test (6MWT) with and without music. METHODS A controlled experiment was conducted using the Ambulosono device to standardize walking exercises. 54 high school students participated, undergoing the 6MWT in different scenarios: Verbal 6-Minute Walking Test (6MWT) or Music-Guided Walking (MU). Final data from 43 students was used in the analysis. The SST measured cognitive changes in both single-task and dual-task conditions. RESULTS The 6MWT significantly enhanced cognitive performance in both single and dual-task conditions. However, the addition of music did not show a substantial improvement in cognitive performance. The findings indicated the positive impact of 6MWT on cognitive abilities, irrespective of musical accompaniment. CONCLUSIONS This research contributes to the understanding of how physical exercises can modulate cognitive functions in healthy individuals. It highlights the potential of 6MWT in enhancing cognitive performance, suggesting further exploration into the role of physical activity in cognitive health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".