The Effects of Personalized Music Listening on Older Adults with Dementia: A Literature Review
Bibliographic record
Abstract
Introduction: Dementia affects millions of people globally and can impact the emotional and cognitive well-being of these individuals. Interventions such as music therapy, including the use of personalized music, are increasingly being used to help reduce the severity of symptoms and enhance patient care. Research has shown that music is strongly associated with long-term memory, and the use of familiar songs may trigger emotional arousal and past memories in individuals with dementia. However, the effectiveness of listening to personalized music on alleviating dementia symptoms is unclear. The aim of this review was to examine the effects of listening to personalized music on emotional arousal and mood in individuals with dementia. Methods: Two databases, Embase and PubMed, were searched for articles exploring personalized or preferred music listening in older adults with dementia and were screened by two co-authors. Results: A total of 9 studies were included in the review. Five of the included studies found positive impacts on mood, such as increased happiness. Two studies demonstrated decreases in agitation, and two studies demonstrated decreases in anxiety. Discussion: This review found an overall positive impact of listening to personalized music on emotion and behavioural and psychological symptoms in dementia (BPSD). The improved emotional arousal and mood shown in this review may lead to enhanced motivation during cognitive tasks, ultimately improving overall performance. Conclusion: Although listening to personalized music was found to strengthen emotional affect and mood, there are inconsistencies in the parameters used during musical interventions. Thus, it is evident that further research is required to determine the optimal guidelines for implementing personalized music listening interventions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".