Association Between Depressive Symptoms, Cognitive Status, and the Dual-Task Performance Index in Older Adults: A Cross-Sectional Study
Bibliographic record
Abstract
The Performance Index (P-Index) is a measure for evaluating mobility-related dual-task performance in older adults. The identification of specific clinicodemographic factors predictive of P-Index scores, however, remains unclear. This cross-sectional study analyzed data from 120 community-dwelling older adults (average age 71.3 ± 11.23 years) to explore clinicodemographic variables that influence P-Index scores during the instrumented timed up and go test. Unadjusted analyses suggested several factors, including age, gender, body mass index, Mini-Mental Status Examination scores, functional reach test performance, history of falls, ethnicity, Geriatric Depression Scale scores, alcohol consumption, and educational levels, as potential predictors of P-Index. However, adjusted multinomial multiple regression analysis revealed Geriatric Depression Scale and Mini-Mental Status Examination scores as the exclusive independent predictors of P-Index classifications, segmented into high, intermediate, or low (percentiles ≤ 25, 26-74, or ≥ 75, respectively). A significant association was observed between the manifestation of depressive symptoms, lower Mini-Mental Status Examination scores, and reduced cognitive-motor performance. The findings implicate depressive symptoms and low cognitive performance as substantial impediments to optimal dual-task mobility within this cohort. Further studies are warranted to examine the efficacy of cognitive stimulation and antidepressant therapy, in augmenting mobility-related dual-task performance among older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".