Motor Dual-Task Deficits and Their Associations with Executive Function in Older Adults with Cognitive Impairments
Bibliographic record
Abstract
Age-related manual dexterity impairments may critically depend on cognitive impairments. This study examined associations between task demands, cognitive function and dexterity impairments in older adults. A total of 42 participants, 21 young (age 19–39; 12 female, 9 male) and 21 older (age 65–88; 12 female and 9 males) adults performed the Grooved Pegboard test under single-task, dual task (visuospatial and nonspatial tasks) and bilateral (finger-tapping test) conditions. Cognitive and executive function were assessed with the Montreal Cognitive Assessment (MoCA) and Trail Making Test (TMT). Older adults showed uniform declines in Grooved Pegboard performance across secondary tasks compared to young adults. Older adults with cognitive deficits (MoCA < 26) took 34% longer to complete the Grooved Pegboard during the nonspatial task compared to healthy older adults. The bilateral task revealed Grooved Pegboard and finger tapping impairments in older adults, particularly those with cognitive deficits, relative to young adults. Significant correlations between increased TMT B completion time and decreased Grooved Pegboard (r = 0.732) and finger-tapping (r = −0.663) performance highlights a relationship between executive function and dexterity impairments in older adults with cognitive deficits. Results suggest the role of cognitive impairments in motor function and the use of task-specific motor assessments.
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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 | 0.002 |
| 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 | 0.000 | 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".