Age-Related Effects on Cognitive-Locomotor Dual-Task Abilities in Activities Representative of Daily Life Among Young Seniors
Bibliographic record
Abstract
Objective: This study examined whether dual-task (DT) cognitive-locomotor interferences are present among young seniors (55–75 years) simultaneously performing a locomotor and a cognitive task of varying levels of complexity while ambulating in a virtual community environment. Method: To assess DT abilities, participants were asked to walk down a virtual mall corridor while remembering a 5-item shopping list. Two levels of complexity for the locomotor (without vs. with obstacles) and the cognitive task (unmodified vs. modified shopping list) were assessed. After measuring the presence of locomotor and cognitive DT costs (DTC) using one sample Wilcoxon signed-rank tests, a nonparametric ANOVA was performed to explore the impact of task complexity on DTC. Spearman coefficients were used to examine the impact of age on DTC. Results: Sixteen participants were recruited. Locomotor and cognitive DTC were observed in all DT conditions, except the easiest combination (no obstacle + unmodified shopping list). These DTC were mainly impacted by the complexity of the cognitive task. They were also positively correlated to age. Discussion: The results highlight the importance of real-life scenarios for accurately describing DT abilities for whom locomotor DTC seems to increase with age despite the absence of daily limitations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".