Does GERAS DANCE improve gait in older adults?
Bibliographic record
Abstract
Older adults at increased fall risk walk slower with less rhythm and coordination. This study explored the effect of dance on spatiotemporal gait parameters in older adults. Participants (N = 23) in a single-arm trial were enrolled in the 12-week GERAS DANCE program (total dose of 36 h). Gait was assessed at baseline and 12-weeks under two experimental conditions: normal walking or walking while performing serial subtraction by 3. Outcome measures were gait speed, stride length time, double support time, stride length and stride width. Separate two-way repeated measures ANOVAs were used to determine estimates of the magnitude of effect of GERAS DANCE on spatiotemporal gait parameters and a paired t-test was used to examine differences in cognitive performance before and after dance. Older adults (72.50 ± 7.13 years; 75% female) had excellent attendance (82.73% adherence). GERAS DANCE resulted in increased gait speed (p<0.001), reduced stride length time (p = 0.034), reduced double support time (p = 0.001), increased stride length (p = 0.003) and marginally reduced stride width (p = 0.064), regardless of the experimental condition. Improvements in walking performance were observed with sustained performance on the serial subtraction task before and after GERAS DANCE. GERAS DANCE may be a safe and feasible program to help improve spatiotemporal gait parameters in older adults with early memory or mobility impairments. Next steps include testing the efficacy for fall prevention to help inform clinical practice guidelines and virtual intervention implementation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".