Associations of reading language preference with muscle strength and physical performance: Findings from the Integrated Women’s Health Programme (IWHP)
Bibliographic record
Abstract
BACKGROUND: The contribution of language preference and ethnicity to muscle strength and physical performance is unclear. We examined the associations of reading language preferences with muscle strength and performance in Chinese women and compared them to other ethnicities. METHODS: The Integrated Women's Health Programme (IWHP) cohort comprised community-dwelling, midlife Singaporean women aged 45-69. Ethnic Chinese women could choose between the English or Chinese versions of the questionnaire. Malay and Indian women were presented with the English version. Sociodemographic, reproductive, anthropometric characteristics were obtained. Hand grip strength and physical performance were objectively assessed. Visceral adiposity (VAT) was determined by Dual-energy X-ray Absorptiometry. Multivariable logistic regression models were used to determine independent associations of language preference/ethnicity with muscle strength and physical performance. RESULTS: The cohort comprised 1164 women (mean age: 56.3±6.2 years); 84.1% Chinese, 5.6% Malay, and 10.3% Indian. 315 Chinese participants (32.2%) had a Chinese-language reading preference (CLP). CLP women tended to be parous, of a lower socioeconomic status (lower proportions received tertiary education, lower employment rates and lower household income), and engaged in less physical activity compared to Chinese women with an English-language preference (ELP). This translated to a weaker hand grip strength (aOR: 1.56; 95%CI: 1.07-2.27), slower repeated chair stand (1.55; 1.12-2.13), poorer balance on tandem stand (2.00; 1.16-3.47), and a slower gait speed (1.62; 1.06-2.47). Compared to ELP women, Malay women had higher odds of poor hand grip strength (1.81; 1.12-2.93) while Indians had a higher odd of poor balance on one-leg stand (2.12; 1.28-3.52) and slow gait speeds on usual (1.88; 1.09-3.25) and narrow walks (1.91; 1.15-3.17). CONCLUSIONS: Chinese language reading preference was associated with inferior muscle strength and physical performance. Such disparities were largest and most consistent in the CLP group, followed by Indian and Malay women compared to the ELP group. Further studies should determine if CLP-associated muscle weakness can predict adverse health outcomes.
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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) | 0.000 | 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 | 0.001 |
| 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".