Benefits of Cognitive Dual-Task Training on Balance Performance in Healthy Older Adults
Bibliographic record
Abstract
BACKGROUND: There is growing evidence of the involvement of executive control in the maintenance of balance in old age. We examined whether healthy older adults who completed five sessions of nonmotor cognitive dual-task training would show significant improvements on measures of dual-task standing balance and mobility, compared with an untrained control group. METHODS: Twenty healthy older adults were assigned to either training or control groups. In the pre- and post-training sessions, all participants performed tests of cognition, balance, and mobility (single-support balance, dynamic posturography, sit-to-stand, 40-foot walk) under single- and dual-task conditions. The training group completed five sessions of cognitive dual-task training spaced at least 2 days apart. The two tasks involved making two-choice decisions to visually presented stimuli. Participants completed multiple blocks of single-task (task A or B, blockwise) and mixed (A, B, or A + B) trials in each training session. RESULTS: The training group showed significant improvements in body sway during single-support balance and center of gravity alignment during double-support dynamic balance. The control group showed no appreciable improvements. CONCLUSIONS: This study is the first to demonstrate training-related benefits to gross motor performance stemming from cognitive dual-task training. The results support the view that motor control in aging is influenced by executive control and have implications for theories of cognitive training and transfer.
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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) | 0.000 | 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 | 0.000 | 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".