Gait and Age‐Related Hearing Loss Interactions on Global Cognition and Falls
Bibliographic record
Abstract
OBJECTIVES: Age-related hearing loss (ARHL) is considered a risk factor for cognitive impairment and falls. The association may be modulated by gait performance because ARHL is related to mobility decline, which strongly contributes to cognitive impairment and falls. We investigated the interactive effects of gait and ARHL on global cognition and falls among older adults. STUDY DESIGN: Retrospective cohort study. METHODS: The auditory acuity of 810 community-dwelling older adults was measured using a pure-tone average of hearing thresholds at 1,000 and 4,000 Hz in the better-hearing ear. Participants were then stratified as follows: normal hearing, ≤25 dB; mild hearing loss (HL), >25 and ≤40 dB; and moderate to severe HL, >40 dB. Gait speed was assessed as an indicator of gait performance and fall occurrence within the previous year. Global cognition was determined using the Montreal Cognitive Assessment (MoCA) test. RESULTS: A total of 320 (39.5%) and 233 (28.8%) participants had mild and moderate to severe HL, respectively. Hierarchical multiple and logistic regression analyses showed interactions between gait performance and moderate hearing loss on both global cognition and the occurrence of falls. Specifically, older adults with moderate hearing loss who walked slowly showed lower MoCA scores and a higher incidence of falls, whereas those with decent gait speed did not show such a tendency. CONCLUSION: Our results suggest that poor gait performance might modulate the effects of ARHL, leading to cognitive decline and falls. Poor cognitive performance and falls may be prevalent in older adults with ARHL, especially in those with slower gait and moderate hearing loss. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:857-863, 2022.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".