The Associations between Hearing, Cognition, and Mobility among Healthy Older Adults with Normal Cognition and Mobility
Bibliographic record
Abstract
Aging is often associated with declines in sensory (e.g., hearing), cognitive, and motor functioning. These functions jointly influence one’s ability to engage successfully in everyday activities (i.e., communication) and avoid injury (i.e., falls-related injuries). Previous studies have typically assessed these abilities independently of each other (rather than holistically), focused on impairments (rather than healthy older adults) and used objective assessments (rather than subjective assessments). This study aimed to fill these gaps by examining the hearing-cognition-mobility link with healthy older adult participants ranging in hearing abilities. Retrospective analyses were conducted on an existing dataset collected within our laboratory at Toronto Rehabilitation Institute, which included an in-person objective-measures session (n= 122) and an online subjective-measures session (n= 41) measuring hearing, cognition and mobility. We conducted bivariate correlations and found that better objective hearing was associated with better objective cognition, better subjective hearing was associated with better subjective cognition and better subjective mobility, and poorer subjective cognition was associated with better objective mobility. Using a series of stepwise backward linear regressions, we found that better subjective hearing predicted both better subjective cognition and better subjective mobility. These findings may have implications for early screening and intervention strategies for adults experiencing subtle sensory-cognitive-motor declines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".