Identifying Balance and Fall Risk in Community-Dwelling Older Women: The Effect of Executive Function on Postural Control
Bibliographic record
Abstract
PURPOSE: The mechanisms linking cognition, balance function, and fall risk among older adults are not fully understood. An evaluation of the effect of cognition on balance tests commonly used in clinical practice to assess community-dwelling older adults could enhance the identification of at-risk individuals. The study aimed to determine (1) the association between cognition and clinical tests of balance and (2) the relationship between executive function (EF) and balance under single- and dual-task testing. METHODS: Participants (24 women, mean age of 76.18 [SD 16.45] years) completed six clinical balance tests, four cognitive tests, and two measures of physical function. RESULTS: Poor balance function was associated with poor performance on cognitive testing of EF. In addition, the association with EF was strongest under the dual-task timed up-and-go (TUG) test and the Fullerton Advanced Balance Scale. Measures of global cognition were associated only with the dual-task performance of the TUG. Postural sway measured with the Standing Balance Test, under single- or dual-task test conditions, was not associated with cognition. CONCLUSIONS: Decreased EF was associated with worse performance on functional measures of balance. The relationship between EF and balance was more pronounced with dual-task testing using a complex cognitive task combined with the TUG.
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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 | 0.005 |
| 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".