COGNITIVE AND PHYSICAL ACTIVITY TRAINING IMPROVES DUAL-TASK PERFORMANCES THROUGH SPECIFIC MECHANISMS
Bibliographic record
Abstract
Previous studies have shown that physical activity and cognitive training can help improve age-related deficits in attentional control. However, it is not yet clear how physical activity-induced improvements compare to cognitive training improvements in attentional control. To investigate this, 68 healthy sedentary participants over the age of 60 (M=68.58, SD=4.68) have been randomized to one of the three 12-week training programs (Aerobic (AE)=23, Motor Functions (MF)=24, Cognition(COG)=21). Before and after the training program, the participants underwent physical fitness tests, and cognitive evaluations (MMSE, and a computerized cognitive dual task – DT). The AE consisted of high intensity training on a recumbent bicycle. The MF consisted of full-body exercises focusing on coordination, balance, stretching, flexibility without raising the heart rate. The COG training consisted of Ipad exercises focusing on executive functions. Repeated measures ANOVAs revealed a decrease in DT cost only in the two physical activity groups (F(2,65)=3.88, P<.03), and no change in task set cost in either of the groups. On all components of the dual-task, reaction time (RT) was improved only in the MF and COG groups (F(2,65)=6.15, P<.00 for dual-mixed trials; F(2,65)=12.54, P<.00; for single-mixed trials; F(2,65)=14.00, P<.00 for single-pure trials), with COG having the highest improvement in all cases. Although the cognitive training improved overall RT the most, the current results suggest that physical activity training might have a superior benefit on task-coordination ability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".