Association Between Timed Up‐and‐Go and Memory, Executive Function, and Processing Speed
Bibliographic record
Abstract
OBJECTIVES: To determine which cognitive tests are independently associated with performance on the Timed Up-and-Go Test (TUG). DESIGN: Data were obtained from Wave 1 of The Irish Longitudinal Study on Ageing (TILDA), a population-based study assessing health, economic, and social aspects of aging. SETTING: Community-dwelling adults completed a home based interview and a health center-based assessment. PARTICIPANTS: TILDA participants aged 50 and older with a Mini-Mental State Examination (MMSE) score of 10 or greater (N = 4,998). MEASUREMENTS: Participants completed a battery of cognitive assessments including the Montreal Cognitive Assessment (MoCA), Color Trails Test, word and letter fluency, choice reaction time, sustained attention, prospective memory, word recall, and picture memory. Linear regression was used to determine univariate and multivariate associations between TUG and each cognitive test. RESULTS: Slower TUG time was associated with poorer performance on all cognitive tests in univariate analysis (P < .05). In multivariate analysis, poorer performance on the MoCA, letter fluency, Color Trail 1, cognitive reaction time, mean sustained attention response time, and prospective memory were independently associated with slower TUG time (P < .05). CONCLUSION: Slower TUG time is independently associated with poorer performance on global cognition, executive function, and memory tests and slower processing speed. This highlights that TUG is more than just a simple mobility task and suggests that a comprehensive cognitive assessment is important for individuals with mobility difficulties.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".