The relationship between dual-task and cognitive performance among elderly participants who exercise regularly
Bibliographic record
Abstract
BACKGROUND: The dual-task performance is associated with the functionality of the elderly and it becomes more complex with age. OBJECTIVE: To investigate the relationship between the Timed Up and Go dual task (TUG-DT) and cognitive tests among elderly participants who exercise regularly. METHOD: This study examined 98 non-institutionalized people over 60 years old who exercised regularly. Participants were assessed using the TUG-DT (i.e. doing the TUG while listing the days of the week in reverse order), the Montreal Cognitive Assessment (MoCA), the Clock Drawing Test (CDT), and the Mini Mental State Examination (MMSE). The motor (i.e. time and number of steps) and cognitive (i.e. number of correct words) data were collected from TUG-DT. We used a significance level of α=0.05 and SPSS 17.0 for all data analyses. RESULTS: This current elderly sample featured a predominance of women (69.4%) who were highly educated (median=10 years of education) compared to Brazilian population and mostly non-fallers (86.7%). The volunteers showed a good performance on the TUG-DT and the other cognitive tests, except the MoCA, with scores below the cutoff of 26 points. Significant and weak correlations were observed between the TUG-DT (time) and the visuo-spatial/executive domain of the MoCA and the MMSE. The cognitive component of the TUG-DT showed strong correlations between the total MoCA performance score and its visuo-spatial/executive domain. CONCLUSIONS: The use of the TUG-DT to assess cognition is promising; however, the use of more challenging cognitive tasks should be considered when the study population has a high level of education.
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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 | 0.003 |
| 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 | 0.001 | 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".