P3–296: Music therapy, global affect and behavior in Alzheimer's disease: A meta‐analytic perspective on outcomes and on music therapy methodologies
Bibliographic record
Abstract
Seminal work has suggested a role for music therapy (MT) in the management of dementia. A 2006 Cochrane review reported improvements of specific dementia symptoms in the behavioral, social, cognitive, and emotional domains. Here a meta-analysis is conducted to determine the effects of MT on global affect and behavior in Alzheimer Disease (AD), and relate these to aspects of MT methodology. To our knowledge, this is a novel investigation of MT outcomes. We have queried electronic search engines for controlled trials of MT in AD. Retrieved studies were included if 1) MT was used as an intervention; 2) Subjects had AD; 3) An AD control group was included for comparison. Exclusions were: a) Mixed treatment (e.g., multiple sensory interventions); b) Mixed subject group (e.g., non-AD dementia, mixed AD); c) Unmatched control group (e.g., Vascular Dementia vs. AD). Studies were assessed if assessment scales on global affect, and behavior were used and five a priori selected MT methodologies reported on. The weighted effect-size (ES) estimate (Hedges' g) by the random effect model (REM) for global affect (assessed on depression, anxiety and mood scales including the GDS) was small in magnitude and non-significant (ES: 0.266; P-value = 0.405; N=6, n-total=259). Classical fail-safe N, using two-tail showed that with only one more study the result would shift significantly. Sub-analysis for global affect by MT methodologies including Input into music selection, Live music, and Patient participation were small to non-existent in magnitude. The ES for Behavior (assessed on NPI, BEHAVE-AD and Confusion Assessment Method Instrument) by the REM was large in magnitude, heterogeneous, and significant (ES: 0.785; P-value = 0.000; N= 15, n-total=714). The sub-analysis on behavior by MT methodologies showed significant and non-heterogeneous ES for: Patient participation (Yes: ES: 0.521; N=10) and Group treatment (Yes: ES: 0.558; N=10). Input into music selection (Yes/No) yielded mixed results across studies. MT benefitted significantly more behavioral than affective symptoms in AD.A caveat to the current meta-analysis is the limited number of studies reporting high quality detailed data for evaluation. Future MT trials examining specific therapeutic methodologies in relation to behavioral and affective functioning are warranted.
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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 | 0.045 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.026 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".