Exploring the impact of community-based choral participation on cognitive function and well-being for persons with dementia: evidence from the Voices in Motion project
Bibliographic record
Abstract
OBJECTIVES: music-based interventions facilitate cognitive benefits remain unknown. The present study examines whether a choral intervention can modulate patterns of cognitive change in persons with dementia and whether within-person variation in affect is associated with this change. METHODS: Thirty-three older adults with dementia engaged weekly in the Voices in Motion (ViM) study consisting of 3 choral seasons spanning 18-months. Performance on the Mini-Mental State Examination (MMSE) and the Positive and Negative Affect Schedule was assessed monthly within each choral season using a longitudinal intensive repeated-measures design. Three-level multilevel models were employed to disaggregate between- and within-person effects across short- (month-to-month) and long-term (season-to-season) intervals. RESULTS: ViM participants exhibited an annual MMSE decline of 1.8 units, notably less than the clinically meaningful 3.3 units indicated by non-intervention literature. Further, variability in negative affect shared a within-person time-varying association with MMSE performance; decreases in negative affect, relative to one's personal average, were linked to corresponding improvements in cognitive function. CONCLUSION: Engagement in the ViM choral intervention may attenuate cognitive decline for persons with dementia via a reduction of psychological comorbidities such as elevated negative affect.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".