A Comparison of the Effect of Physical Activity and Cognitive Training on Dual-Task Performance in Older Adults
Bibliographic record
Abstract
OBJECTIVES: Studies suggest that cognitive training and physical activity can improve age-related deficits in dual-task performances. However, both of these interventions have never been compared in the same study. This article investigates the improvement in dual-task performance in 2 types of exercise training groups and a cognitive training group and explores if there are specific dual-task components that are more sensitive or more likely to improve following each type of training. METHODS: Seventy-eight healthy inactive participants older than the age of 60 (M = 69.98, SD = 5.56) were randomized to one of three 12-week training programs: aerobic training (AET) = 26, gross motor abilities (GMA) = 27, and cognition (COG) = 25. Before and after the training program, the participants underwent physical fitness tests, and cognitive evaluations involving a computerized cognitive dual task. The AET consisted of high- and low-intensity aerobic training, the GMA of full-body exercises focusing on agility, balance, coordination, and stretching, and the COG of tablet-based exercises focusing on executive functions. RESULTS: Repeated-measures analysis of variance on reaction time data revealed a group × time interaction (F(2,75) = 11.91, p < .01) with COG having the greatest improvement, followed by a significant improvement in the GMA group. Secondary analysis revealed the COG to also improve the intraindividual variability in reaction time (F(1,24) = 8.62, p < .01), while the GMA improved the dual-task cost (F(1,26) = 12.74, p < .01). DISCUSSION: The results show that physical and cognitive training can help enhance dual-task performance by improving different aspects of the task, suggesting that different mechanisms are in play.
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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 | 0.000 |
| 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".