Does a Live Performance Impact Synchronization to Musical Rhythm in Cognitively Impaired Elderly?
Bibliographic record
Abstract
BACKGROUND: Music-based interventions appear to be efficient approaches to improve emotional, social, and cognitive functioning of patients with neurodegenerative diseases. OBJECTIVE: Because benefits seem to increase with patient's motor involvement, we studied sensorimotor synchronization (SMS) abilities of patients with cognitive impairments (Alzheimer's disease, vascular and mixed dementia) and of patients with no evidence of cognitive impairments. More specifically, we compared the impact of a live performance by a musician to a video recording on SMS. METHODS: SMS to a metronomic or a musical stimulus was assessed while patients watched a live musician or his pre-recorded video. RESULTS: SMS to a metronome was better than to music but this effect was modulated by the social context. While SMS to a metronome was better when facing a video than a live performance, there was no impact of social context on SMS to music. No group differences of SMS were found. CONCLUSION: The decrease in SMS to a metronome in a live performance may be due to social pressure. Such a pressure might be removed in pleasant social activities, like moving with music in a group, explaining the lack of effect on SMS to music. We found no performance differences in groups, suggesting relatively spared SMS in cognitively impaired patients. By showing that it is possible to encourage patients to synchronize with others, even when facing a video, our results indicate that SMS can be used as a relevant predictor in clinical trials and open up promising therapeutic options for isolated patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".