Association of Perceived Acoustic Environment Exposure and Nonrestorative Sleep with Cognitive Functioning Among Chinese Adults: A Cross-Sectional Survey
Bibliographic record
Abstract
Despite the growing emphasis on cognitive health, evidence regarding individual and environmental factors influencing cognitive functioning remains limited. We aimed to examine the association of personal and environmental factors, specifically perceived acoustic environment exposure and nonrestorative sleep, with cognitive functioning among Chinese adults. Between July and August 2022, we recruited 970 Chinese adults from all districts in Hong Kong for our cross-sectional study. Univariable and structured multiphase linear regression analyses were conducted to identify the contributory factors. Among 970 Chinese adults, the structured multiphase linear regression model revealed that being in their 30s (β = 0.160; 95% CI: 0.004, 0.315) and 40s (β = 0.186; 95% CI: 0.029, 0.343), being female (β = 0.095; 95% CI: 0.018, 0.173), being former smokers, and having medical consultations, medical histories (eczema, hearing problems, and insomnia), perceived acoustic environment exposure (traffic and mechanical sounds (β = 0.011; 95% CI: 0.008, 0.013), nature and music sounds (β = 0.004; 95% CI: 0.001, 0.007), and human sounds (β = 0.002; 95% CI: 0.0004, 0.005)), and psychological symptoms (perceived stress, depressive, and anxiety symptoms) were associated with worse cognitive functioning. Moreover, more nonrestorative sleep (β = -0.015; 95% CI: -0.022, -0.007) was also associated with worse cognitive functioning. This study revealed that increased perceived acoustic environment exposure and a higher degree of nonrestorative sleep were associated with poorer cognitive functioning among Chinese adults. This underscores the need for public health strategies and policies aimed at fostering a healthy acoustic environment and promoting sleep hygiene education in the community.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".