Effects of Music Listening on Cognition and Affective State in Older Adults
Bibliographic record
Abstract
Abstract: This systematic review and meta-analysis examined whether and how music listening impacts cognition and affect in healthy older adults, specifically considering the emotional connotations of music (happy- or sad-sounding music) and its presentation modality (background or prior to the tasks). Based on the PRISMA guidelines and preregistering in PROSPERO (CRD42022366520), we searched the Scopus, PsycInfo, and Web of Science databases. Out of 2,675 articles, 27 met the inclusion criteria. The synthesized findings on cognition (23 studies) revealed an uncertain influence of music type and presentation modalities on memory outcomes. In contrast, happy-sounding music seems to support executive functioning (2 out of 4) and processing speed (1), when presented in the background, and facilitate language processes (2 out of 3), when given prior to the task. However, the high heterogeneity and inconsistency in the music type and presentation modalities, as well as in the cognitive outcomes considered, prevented us from drawing clear conclusions on the effect of music listening on older adults’ cognition. For affective outcomes, a narrative synthesis of the findings on mood (12 studies) and arousal (7 studies) outcomes showed that, regardless of music presentation modality, happy- and sad-sounding music increase or decrease mood/valence and arousal, respectively. Results from meta-analysis showed no significant cognitive benefits from music listening (SMD = 0.09, [95% CI: −0.17, 0.35], p = 0.51) and suggest a positive effect of happy-sounding music on arousal (SMD = 0.44 [95% CI: 0.13, 0.74], p = 0.005), but not on valence (SMD = 0.79 [95% CI: −0.25, 1.84], p = 0.14). The methodological shortcomings of the extant literature call upon the need for further studies adopting more rigorous and consistent approaches that better elucidate the potential benefits of music listening on cognitive and affective outcomes among older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".