Postural Sway, Falls, and Cognitive Status: A Cross-Sectional Study among Older Adults
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment-related changes in postural sway increase fall risk among older adults. Better understanding this association could be helpful for fall prevention. OBJECTIVE: To examine the center-of-pressure (COP) velocity association with cognitive status and history of falls, in cognitively healthy individuals (CHI), patients with mild cognitive impairment (MCI), and with mild-to-moderate Alzheimer's disease (MMAD). METHODS: Six hundred and eleven older community-dwellers (77.2 ± 7.9 years; 51.8% men) were separated into CHI, MCI, and MMAD participants. By computing the average absolute maximal velocity (AAMV), the bounding limits of COP velocity dynamics were determined while participants were asked to maintain quiet stance on a force platform with eyes open or with eyes closed. Age, gender, history of falls, body mass index, medications, handgrip strength, Timed Up & Go score were used as covariates. RESULTS: The multivariate ANCOVA, with AAMV in eyes open and eyes closed conditions as dependent variables, showed that the highest AAMVs that bound the COP velocity dynamics of postural sway were associated with cognitive impairment (p = 0.048) (i.e., lowest limits in CHI and MCI as compared with MMAD) and falls (p = 0.033) (i.e., highest limits in fallers). CONCLUSIONS: These findings identified the bounding limits of COP velocity as a hallmark feature of cognitive impairment-related changes in postural sway, in particular for MMAD. This point is of special interest for clinical balance assessment and fall prevention in MMAD patients in order to plan long-term targeted fall-prevention programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".