Soundscape augmentation as a nonpharmacological intervention to reduce the behavioural effect of dementia: a randomized controlled trial
Bibliographic record
Abstract
Soundscape is an important environmental factor that influences people's behaviour and well-being. People with dementia can benefit from a personalized soundscape to improve the behavioural and psychological syndrome of dementia. This paper is based on a single-blind, repeated-measure pilot randomized clinical trial of soundscape augmentation on the treatment effect of people with severe dementia. Participants were randomized to a personalized soundscape intervention delivered in their room in the morning and evening or treatment-as-usual, with two baseline weeks and four weekly post-randomization assessments of the primary and secondary behavioural outcomes. A linear mixed effects model showed that the soundscape intervention was possible and acceptable. There were improvements in the Neuropsychiatric Inventory (resistance to care) and a significant reduction in aggression in the soundscape group. This study is the first pilot randomized controlled trial of a soundscape intervention for older adults with dementia to improve the behavioural and psychological syndrome of dementia. In this pilot study, soundscape augmentation was a feasible and effective non-pharmacological approach to reducing resistance and improving participants' behaviour. Further studies with larger samples are recommended to confirm our findings. Longitudinal studies are necessary to investigate the long-term effect of an augmented sonic environment on people with dementia.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".