Global Cognition, Gender, and Level of Education Predict Dual-Task Gait Speed Variability Metrics in Older Adults
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to determine if demographic variables and measures of cognitive function, functional mobility, self-reported balance self-efficacy, and self-reported physical activity can predict gait speed variability during single-task walking (STGSCV), during cognitive-motor dual-tasking (DTGSCV), and dual-task effect on gait speed variability (DTEGSCV) in older adults. METHODS: In 62 older adults, demographics were recorded and cognitive function (including the Montreal Cognitive Assessment, MoCA), functional mobility, balance self-efficacy (Activities-specific Balance Confidence Scale, ABC), and self-reported physical activity (Physical Activity Scale for the Elderly, PASE) were assessed. Three linear regression models were used to determine whether the functional mobility tests, PASE score, ABC score, and tests of cognitive function predicted gait speed variability outcomes (STGSCV, DTGSCV, DTEGSCV), with demographics included as covariates. RESULTS: MoCA score (p = 0.003), gender (p = 0.040), and years of education (p = 0.010) significantly predicted DTGSCV (R2 = 0.297, p = 0.002). MoCA score (p = 0.008) and years of education (p < 0.001) also significantly predicted DTEGSCV. Despite a significant regression model (R2 = 0.316, p = 0.001), there were no significant individual predictors of STGSCV. CONCLUSION: Older adults with lower cognitive function scores, according to the MoCA, and higher levels of education demonstrate larger gait speed variability during dual-tasking and may be at increased risk of real-world mobility issues or falling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".