Choir Singing and Music Appreciation Training Enhances Unaided Speech-in-Noise Perception and Frequency Following Responses for Older Adult Hearing Aid Users: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Hearing aids (HAs) improve speech perception in quiet environments but remain less effective in noisy conditions, posing significant communication challenges for older adults. Musical training has been proposed as a potential intervention to enhance speech-in-noise (SIN) perception through auditory neuroplasticity. This randomized controlled trial investigated the impact of a 14-week music-based intervention on auditory outcomes in older adult HA users. Forty-seven participants were randomly assigned to one of three groups: choir singing (n = 14; active music training), music appreciation (n = 13; passive music engagement), or a do-nothing control group (n = 12). Primary outcome measures included SIN perception, while secondary outcomes assessed pitch perception and frequency-following response (FFR). Results revealed that participants in the choir singing group demonstrated significant improvements in unaided SIN perception and FFR compared to the do-nothing control group, but not in aided conditions. No significant differences were found between the choir singing and music appreciation groups, suggesting that both active and passive music engagement may enhance auditory processing. These findings highlight the potential of music-based training as a complementary intervention for older adults with hearing loss, though further research is needed to establish long-term benefits and effects in everyday listening conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".