Effects of GERAS DANCE on cognitive function in older adults with and without cognitive impairment
Bibliographic record
Abstract
Abstract Background Mild cognitive impairment (MCI) is highly prevalent in older adults aged 60+ with prevalence rates of 16‐20% and one of the first cognitive expressions of Alzheimer's Disease and other dementias. Dance activates many brain regions to integrate complex movement patterns with musical and emotional expression and may lower the risk of dementia over time. We investigated the effect of 12‐weeks of GERAS DANcing for Cognition and Exercise (DANCE) on cognitive function in older adults (aged 60+) with and without cognitive impairment. Methods GERAS DANCE is a specialised program tailored for older adults with early memory or mobility problems. It was offered across 12 YMCAs in Southern Ontario, Canada with in‐person classes 2x weekly (1‐hour each) and homework (10‐minutes daily) for a period of 12 weeks. Cognitive function was assessed with the Montreal Cognitive Assessment [MOCA]. Changes in cognitive function from baseline to 12 weeks were evaluated using ANOVA [2 (Time: pre‐ post‐intervention) x 2 (Group: cognitive impairment, no cognitive impairment)]. Result A total of 106 older adults participated in the analysis (mean age = 76.12 ± 7.03, range 61 to 93 years, 81% female). Overall GERAS DANCE improved cognitive function [F(1,105) = 4.928, p = 0.029]. In older adults with cognitive impairment, the mean difference of MOCA total scores improved by 1.04 points (pre = 21.71 ± 3.34, post = 22.74 ± 4,12). Conclusion GERAS DANCE has the potential to improve or maintain cognitive function in older adults with and without dementia. Next steps include testing for efficacy of GERAS DANCE as a part of an overall approach for cognitive health and to inform clinical practice guidelines.
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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 | 0.001 |
| 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 | 0.000 | 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".