Relationship between dual task walking, cognition, and depression in oldest old people
Bibliographic record
Abstract
BACKGROUND: The purpose of the study was to investigate the relationship between dual task walking, cognition, and depression in oldest old people living in the community. METHODS: We conducted an observational cross-sectional study at Federal University of São Carlos (Brazil). We assessed 67 community-dwelling older adults aged 80 years and over, who were able to walk alone and did not present with a risk of dementia (assessed by Mini-Mental State Examination, MMSE). The assessment consisted of anamnesis, dual task using the Timed Up and Go test associated with a motor task (TUGT-motor) and a cognitive task (TUGT-cognitive); cognitive measures using MMSE, Montreal Cognitive Assessment (MoCA), Clock Drawing test (CDT) and verbal fluency, and depressive measures by the Geriatric Depression Scale (GDS). RESULTS: There was a correlation with higher magnitude between cognitive tests and TUGT-cognitive, compared to TUGT-motor. For TUGT-motor, the highest correlations with cognitive tests were found between time and MMSE, MoCA (total score), and MoCA visuospatial/executive domain. For TUGT-cognitive, the highest correlations with cognitive tests were between number of steps and MMSE and between time and MMSE. GDS showed a significant weak correlation with number of steps taken in TUGT-motor, wrong words, and correct/time of TUGT-cognitive. CONCLUSIONS: Dual task performances are associated with cognition in oldest old. Furthermore, dual task tests have less influence of educational level, are functional, fast, and easily applicable in clinical practice. Future studies are needed to confirm if dual task test is useful for cognitive screening in oldest old.
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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 | 0.001 | 0.001 |
| 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".