Synergistic Effects of Cognitive Training and Physical Exercise on Dual-Task Performance in Older Adults
Bibliographic record
Abstract
BACKGROUND: Studies report benefits of physical exercise and cognitive training to enhance cognition in older adults. However, most studies did not compare these interventions to appropriate active controls. Moreover, physical exercise and cognitive training seem to involve different mechanisms of brain plasticity, suggesting a potential synergistic effect on cognition. OBJECTIVE: This study investigated the synergistic effect of cognitive training and aerobic/resistance physical exercise on dual-task performance in older adults. Intervention effects were compared to active controls for both the cognitive and the exercise domain. METHOD: Eighty-seven older adults completed one of 4 different combinations of interventions, in which computer lessons was active control for cognitive training and stretching/toning exercise control for aerobic/resistance training: (a) cognitive dual-task training and aerobic/resistance training (COG+/AER+), (b) computer lessons and aerobic/resistance training (COG-/AER+), (c) cognitive dual-task training and stretching/toning exercises (COG+/AER-), and (d) computer lessons and stretching/toning exercises (COG-/AER-). The primary outcome was performance in an untrained transfer dual task. Stepwise backward removal regression analyses were used to predict pre- versus post-test changes in groups that have completed the dual-task training, aerobic/resistance or both interventions. RESULTS: Participation in AER+ did not predict improvement in any dual-task outcomes. Participation in COG+ predicted reduction in dual-task cost and participation in COG+/AER+ predicted reduction in task-set cost. DISCUSSION: Results suggest that the combination of cognitive and physical training protocols exerted a synergistic effect on task-set cost which reflects the cost of maintaining multiple response alternatives, whereas cognitive training specifically improved dual-task cost, which reflects the ability of synchronizing concurrent tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".