VIRTUAL GERAS DANCING FOR COGNITION EXERCISE (DANCE) FOR OLDER ADULTS: A FEASIBILITY RANDOMIZED CONTROL TRIAL
Bibliographic record
Abstract
Abstract Background GERAS DANcing for Cognition and Exercise (DANCE) was developed with rehabilitation and geriatric medicine expertise for older adults (age 60+) looking to improve brain health or mobility. Our primary aim of this study was to assess the feasibility of virtual GERAS DANCE. Methods This study utilized a single-center, prospective, parallel-group randomized controlled trial (RCT) feasibility approach. We recruited 50 older adults. Participants were randomized to receive 6-weeks (1-hour class twice weekly) of virtual GERAS DANCE or usual care. Feasibility was assessed using pre-defined criteria for process, outcomes, and acceptability, and the effect of GERAS DANCE on mood, balance confidence, and fear of falling. Results Our study recruitment period occurred over 8 weeks to recruit 50 older adults (mean age = 75.02(5.89) years, range: 63-92, 92% female). The enrollment-to-screening ratio was calculated as 25:103 and the retention rate of participants was 84%. The average class attendance of the study cohort was 77%. One adverse event was reported unrelated to the study intervention. The program had a high-fidelity score and adhered to the standardized curriculum. Both the intervention and usual care groups improved mood (Depression, Anxiety and Stress Scale – 21). The intervention-group had higher balance confidence and lower fear of falling. Discussion Pre-determined thresholds for feasibility were met for all outcomes providing evidence that virtual GERAS DANCE is feasible, well-accepted, and safe for older adults. Improved balance confidence and reduced fear of falling through dance can have significant implications for fall prevention, healthcare costs, and overall quality of life.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".