Thinking-While-Moving Exercises May Improve Cognition in Elderly with Mild Cognitive Deficits: A Proof-of-Principle Study
Bibliographic record
Abstract
BACKGROUND: Noninvasive interventions to aid healthy cognitive aging are considered an important healthcare priority. Traditional approaches typically focus on cognitive training or aerobic exercise training. In the current study, we investigate the effect of exercises that directly combine cognitive and motor functions on visuomotor skills and general cognition in elderly with various degrees of cognitive deficits. SUBJECTS AND METHODS: A total of 37 elderly, divided into four groups based on their level of cognition, completed a 16-week cognitive-motor training program. The weekly training sessions consisted of playing a videogame requiring goal-directed hand movements on a computer tablet for 30 minutes. Before and after the training program, all participants completed a test battery to establish their level of cognition and visuomotor skills. RESULTS: We observed an overall change in visuomotor behavior in all groups, as participants completed the tasks faster but less accurately. More importantly, we observed a significant improvement in measures of overall cognition in the subaverage cognition group and the mild-to-moderate cognitive deficits group. CONCLUSION: Our findings indicate that (1) cognitive-motor exercises induce improved test scores, which is most prominent in elderly with only mild cognitive deficits, and (2) cognitive-motor exercises induce altered visuomotor behavior and slight improvements in measures of general cognition.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".