Older males exhibit greater dual-task gait variability and postural amplitude than older females
Bibliographic record
Abstract
Abstract Greater dual-task cost (poorer cognitive and/or motor performance) heightens fall risk in older adults. There may be sex differences in dual-task cost that may inform tailored fall prevention strategies, but further research is needed. The purpose of this study was to examine whether sex differences influence dual-task cost during posture and gait in older males and females with poor mobility. A cross-sectional study was conducted in 85 older adults with self-reported poor mobility with no significant cognitive impairment (aged ≥65 years, 50 females; Montreal Cognitive Assessment scores>18). Using APDM wearable inertial sensors, participants stood with their feet apart and eyes open while completing no cognitive task, and while counting backwards by 3’s. Participants also walked at a comfortable walking speed while completing no cognitive task, and while naming words starting with “F”, “A”, or “S”. Two gait (step time coefficient of variability (CV), stride length CV, gait speed, double-support time, elevation CV) and postural (velocity, root mean square error (RMS), jerk, frequency) trials were completed. Males exhibited a greater step time CV during dual-task gait (F = 5.23, p=.009, males: 68.6±89.4%; females: 26.6±59.5%) and RMS during dual-task posture (F = 1.33, p=.009, males: 2884.5±1701.9m/s2; females: 2020.39±1458.19m/s2) than females in analyses of covariance, controlling for age and height. These findings suggest that greater variability in step time during gait and amplitude of postural sway in males relative to females serve as sensitive markers of dual-task cost in older adults. These results improve understanding of sex-specific differences in dual-task performance, guiding targeted interventions to enhance mobility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 0.001 |
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".