The Effect of a Verbal Cognitive Task on Postural Sway Does Not Persist When the Task Is Over
Bibliographic record
Abstract
Dual-task balance studies explore interference between balance and cognitive tasks. This study is a descriptive analysis of accelerometry balance metrics to determine if a verbal cognitive task influences postural control after the task ends. Fifty-two healthy older adults (75 ± 6 years old, 30 female) performed standing balance and cognitive dual-tasks. An accelerometer recorded movement from before, during, and after the task (reciting every other letter of the alphabet). Thirty-six balance metrics were calculated for each task condition. The effect of the cognitive task on postural control was determined by a generalized linear model. Twelve variables, including anterior-posterior centroid frequency, peak frequency and entropy rate, medial-later entropy rate and wavelet entropy, and bandwidth in all directions, exhibited significant differences between baseline and cognitive task periods, but not between baseline and post-task periods. These results indicate that the verbal cognitive task did alter balance, but did not bring about persistent effects after the task had ended. Traditional balance measurements, i.e., root mean square and normalized path length, notably lacked significance, highlighting the potential to use other accelerometer metrics for the early detection of balance problems. These novel insights into the temporal dynamics of dual-task balance support current dual-task paradigms to reduce fall risk in older adults.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".