Realistic dual-task listening-while-balancing in older adults with normal hearing and hearing loss with and without hearing aids
Bibliographic record
Abstract
Age-related hearing loss is a risk factor for mobility problems and falls, possibly due to poor access to spatial sounds or the higher allocation of attention required to listen, thereby reducing cognitive resources to support mobility. Introducing stabilizing spatial sounds or reducing cognitive load through hearing aids could possibly improve balance performance; however, evidence is mixed. Few studies have evaluated the effects of hearing aids and spatial sounds on balance during realistic, multisensory, dual-tasking conditions. This study used virtual reality to simulate a listening-while-balancing task in 22 older adults with normal hearing and 22 hearing aid users, tested with their aids on versus off. Participants performed a competing digits listening task (two, four digits) and a standing postural task, alone and in combination (dual task) under different visual, postural, and acoustical loads. Listening accuracy and postural outcomes (centre of pressure mean velocity, anterior-posterior standard deviation, medial-lateral standard deviation) were collected. With respect to listening accuracy, as expected, normal hearing adults had higher listening accuracy than those with hearing loss (aided better than unaided) and both groups performed better with eyes closed (vs. open) and under lower postural loads (firm vs. compliant). With respect to postural performance, hearing aids did not remarkably improve balance overall, with no effects on dual-task costs to posture. Other factors related to the complexity of the conditions (i.e., listening, visual, postural demands) differently influenced dual-task costs to posture in individuals with and without hearing loss. Overall, these results contribute to our understanding of how age-related hearing loss and hearing aids affect balance-related outcomes under realistic, complex, multisensory, multitasking 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".