Association of Drinking Herbal Tea with Activities of Daily Living among Elderly: A Latent Class Analysis
Bibliographic record
Abstract
The aim of this study was to explore whether drinking herbal tea and tea would positively benefit activities of daily living (ADL) in the elderly. We used data from the Chinese longitudinal healthy longevity survey (CLHLS) to explore the association. Drinking herbal tea and drinking tea were divided into three groups using latent class analysis (LCA): frequently, occasionally, and rarely. ADL disability was measured by the ADL score. Multivariate COX proportional hazards models with competing risks were used to explore the impact of drinking herbal tea and tea on ADL disability, statistically adjusted for a range of potential confounders. A total of 7441 participants (mean age 81.8 years) were included in this study. The proportions of frequently and occasionally drinking herbal tea were 12.0% and 25.7%, respectively. Additionally, 29.6% and 28.2% of participants reported drinking tea, respectively. Multivariate COX regression showed that compared with rarely drinking, frequently drinking herbal tea could effectively reduce the incidence of ADL disability (HR = 0.85, 95% CI = 0.77–0.93, p = 0.005), whereas tea drinking had a relatively weaker effect (HR = 0.92, 95% CI = 0.83–0.99, p = 0.040). Subgroup analysis found that frequently drinking herbal tea was more protective for males under 80 years old (HR = 0.74 and 0.79, respectively), while frequently drinking tea was somewhat protective for women (HR = 0.92). The results indicate that drinking herbal tea and tea may be associated with a lower incidence of ADL disability. However, the risks associated with using Chinese herb plants still deserve attention.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".