Comparing the effects of an acute bout of physical exercise with an acute bout of interactive mental and physical exercise on electrophysiology and executive functioning in younger and older adults
Bibliographic record
Abstract
BACKGROUND: Physical exercise has been shown to improve cognitive and neural functioning in older adults. AIMS AND METHODS: The current study compared the effects of an acute bout of physical exercise with a bout of interactive mental and physical exercise (i.e., "exergaming") on executive (Stroop) task performance and event-related potential (ERP) amplitudes in younger and older adults. RESULTS: Results revealed enhanced executive task performance in younger and older adults after exercise, with no differences in performance between exercise conditions. Stroop (RT) performance in older adults improved more than in younger adults from pre- to post-exercise. A significant increase in EEG amplitude from pre- to post-exercise was found at the Cz site from 320 to 700 ms post-stimulus for both younger and older adults, with older adults demonstrating a larger Stroop interference effect. While younger adults exhibited overall greater EEG amplitudes than older adults, they showed no differences between congruent and incongruent trials (i.e., minimal interference). Compared to peers with higher BMI (body mass index), older adults with lower BMI showed a greater reduction in Stroop interference effects from pre- to post-exercise. DISCUSSION AND CONCLUSIONS: The beneficial effects of an acute bout of physical exercise on cognitive and neural functioning in younger and older adults were confirmed, with no difference between standard exercise and exergaming. Findings suggest that BMI, sometimes used as a proxy for fitness level, may modulate benefits that older adults derive from an acute bout of exercise. Findings have implications for future research that seeks to investigate unique effects of exergaming when compared to standard physical exercise.
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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) | 0.001 | 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 | 0.001 | 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".